{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://github.com/timeseriesAI/tsai/blob/main/tutorial_nbs/12_Experiment_tracking_with_W%26B.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "created by Ignacio Oguiza - email: timeseriesAI@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Purpose 😇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This brief notebook will demonstrate how you can easily track your experiments using Weights & Biases (W&B).\n",
    "\n",
    "You can find much more information on the W&B [Experiment tracking](https://docs.wandb.ai/guides/track) page."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%capture\n",
    "# # **************** UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI ****************\n",
    "# stable = False # Set to True for lastest pip version or False for main branch in GitHub\n",
    "# !pip install {\"tsai -U\" if stable else \"git+https://github.com/timeseriesAI/tsai.git\"}\n",
    "# !pip install wandb -U\n",
    "# # ⚠️ REMEMBER TO RESTART (NOT RECONNECT/ RESET) THE KERNEL/ RUNTIME ONCE THE INSTALLATION IS FINISHED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os             : Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\n",
      "python         : 3.7.12\n",
      "tsai           : 0.2.23\n",
      "fastai         : 2.5.2\n",
      "fastcore       : 1.3.26\n",
      "wandb          : 0.12.2\n",
      "torch          : 1.9.0+cu102\n",
      "n_cpus         : 2\n",
      "device         : cuda (Tesla T4)\n"
     ]
    }
   ],
   "source": [
    "from tsai.all import *\n",
    "from fastai.callback.wandb import *\n",
    "import wandb\n",
    "my_setup(wandb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Login to W&B 🔎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mtimeseriesai\u001b[0m (use `wandb login --relogin` to force relogin)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a configurable training script 🏋️‍♂️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we'll run experiments with TSiT which is a new model developed by timeseriesAI inspired by ViT.\n",
    "\n",
    "We'll first define a baseline we'll then try to improve: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.080252</td>\n",
       "      <td>1.576666</td>\n",
       "      <td>0.521087</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
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       "      <td>1.614804</td>\n",
       "      <td>1.256952</td>\n",
       "      <td>0.591646</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.358594</td>\n",
       "      <td>1.146953</td>\n",
       "      <td>0.618410</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.193168</td>\n",
       "      <td>1.260768</td>\n",
       "      <td>0.563260</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.088524</td>\n",
       "      <td>1.090860</td>\n",
       "      <td>0.642741</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.979203</td>\n",
       "      <td>1.072997</td>\n",
       "      <td>0.641525</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.880059</td>\n",
       "      <td>0.974234</td>\n",
       "      <td>0.680049</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.788186</td>\n",
       "      <td>1.006785</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.706335</td>\n",
       "      <td>0.917137</td>\n",
       "      <td>0.704380</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.656883</td>\n",
       "      <td>0.916646</td>\n",
       "      <td>0.705596</td>\n",
       "      <td>00:01</td>\n",
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       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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udazieh3w2tWJeMATwHhgL7CPm5l4p5TKn/54F6K/gdYvQ7WOdqdxihwNDhOR8kBdYE26VZlN8Pgz3f6ZTuSoFeZN3YiCfLxoFy0rBBHo65qGz+4yKFxzuFcGzZF5DjsmXKVl14QvO/8bGGPmY/UaTrvspXTfv5LJvhOACRksjwJqOiFbvukodDNtnpXKc6K/hSVvQZ1e0GS43WmcJtuFsYgUAL4Bhhtjbqijf1YTOZ6/pybdv1jFjzvP81izW2/kFDnmLoPCNYd7ZdAcmeewY8JVWlk99jk3XJ1wpf4pICCA2NhYQkND83xxbIwhNjaWgIAAu6MoZb+j6+H7x6FMY7j3Y+vhFHlEtv51ERFfrKJ4mjHm2ww2ycnkkEw1rBBCs8rFGPPHPno1KkuhAN+cHkIppZSLREREEBMTw6lTp1x2ztTUVLy87Hk2VUBAABEROkFc5XPnjsKM3lCgOPSYCj7+We/jQbLTlUKAL4EdxpgPM9lsHjDU0Xi+EXDOMbkjx55rU4V7P13O+GUHeObuyjdyCKWUUi7g6+v7jycOuoK7fIKiVL505SLM7AVX4qHvIihQzO5ETpedX7ubAH2BliKyyfHqICKDRWSwY5v5wH6sSRzjsCZ13JBaEYXpUKskXy7bT2z85Rs9jFJK/aVAgQIAHDt2jAceeCDDbZo3b05UVJQrYymllOdITYXvBsOfW+CBCVAifYOyvCHLO8bGmOXAdQePOGZBD7neNjnxzN2V+SX6OGOW7OPFjnnzB6+Ucr3SpUszZ84cu2MopZTnWfwm7JgHbd6Eym3tTpNr7BmolYWKxQtyf70Ipqw+xJ/nEuyOo5RyMyNHjmT06NF/ff/KK6/wxhtv0KpVK+rVq0etWrWYO/fatrwHDx6kZk2rEUNCQgI9e/akWrVq3HfffSQk6HuNUkplaMssWPY+1OsHtzvtPqhbsv2R0Jl5qlUl5m46yie/7+Wt+2rZHUcplZGfR8LxrVlvlxMla0H7t6+7SY8ePRg+fDhDhlhv0LNmzWLBggUMGzaMQoUKcfr0aRo3bkynTp0y7ZYwZswYgoKC2LFjB1u2bKFevXrOvQ6llMoLjqyFuUOh3J3Q4YM81YEiI255xxigTEgQvRuWZda6Ixw8fdHuOEopN1K3bl1OnjzJsWPH2Lx5M0WLFqVkyZK88MIL1K5dm9atW3P06FFOnDiR6TGWLl1Knz59AKhduza1a3v+E5uUUsqpzh62HvdcqDT0+Ap8/OxOlOvc9o4xwJCWFfk66ggf/bqbj3rWtTuOUiq9LO7s5qZu3boxZ84cjh8/To8ePZg2bRqnTp1i/fr1+Pr6Ur58eRITE23Lp5RSHu3yBZjeE5KvQP+vISh/dINx2zvGAMULBjCgSQXmbj7GzuM39EwRpVQe1aNHD2bOnMmcOXPo1q0b586do3jx4vj6+rJ48WIOHTp03f2bNm3K9OnTAYiOjmbLli2uiK2UUu4vNQW+HQSndkK3iVCsit2JXMatC2OAx5reQgF/Hz5YuNvuKEopN1KjRg0uXLhAeHg4pUqV4sEHHyQqKopatWoxZcoUqlatet39H3/8ceLj46lWrRovvfQS9evXd1FypZRyc7+9CrvmQ7u3oWIru9O4lFsPpQAoEuTHY01v4f2Fu9l4+Ax1yxa1O5JSyk1s3fr3xL+wsDBWrVqV4Xbx8fEAlC9fnujoaAACAwOZOXNm7odUSilPsnEarPgYGgyEho/ancbl3P6OMcCAJhUIDfbj/YW77I6ilFJKKZU3HVoJPzwFtzSH9u/k+Q4UGfGIwjjY34chLSqyYm8sK/aetjuOUkoppVTeEncAZj4IRctBt0ng7Wt3Ilt4RGEM0LtRWUoXDuC9BbuwHrSnlLJLfvp/MD9dq1LqJhmDJJ6zO0XOJZ6HGT3BpELvWRCYf4etekxhHODrzVOtK7HpyFl+3XHS7jhK5VsBAQHExsbmi4LRGENsbCwBAQF2R1FKeYLfXqXo2EiY2hV2L4TUVLsTZS0lGeY8DLF7ofsUCL3V7kS2cvvJd2l1rRfB53/s5/0Fu2hVtTheXvlv7ItSdouIiCAmJoZTp07Zcv7U1FS8vFz3O31AQAAREREuO59SykNt/hqWjyIp4nZ8j0fD9G5QtII1gS3yQQgsYnfCjC36L+xdBB1HwS3N7E5jO48qjH28vXjm7so8OWMjP2w5RufIcLsjKZXv+Pr6UqFCBdvOHxcXR0hI/mg0r5TyEEfXw7wnodydXOj4JSFFQ2DnD7BmLCx4AX5/A2r3gIaDoER1u9P+LWoirP4MGj0ODR62O41b8JihFFfdU6sU1UoV4sNFu0lK8YCPKJRS6iaISDsR2SUie0VkZCbbdBeR7SKyTUSmO5a1EJFNaV6JItLFsW6SiBxIsy7SldekVJ5y/k9r0lrBEtB9Mnj7WY9OrtkVBi6Ax5ZCzfth8wwYcztM6gjb51pDGOx0YCnMfw4qtoY2b9ibxY14XGHs5SU816Yyh2IvMTsqxu44SimVa0TEGxgNtAeqA71EpHq6bSoBzwNNjDE1gOEAxpjFxphIY0wk0BK4BCxMs+uIq+uNMZtccDlK5T1JifD1g9bktZ4zIDjs2m1K1YHOo+GZHdD6VThzCGb1g49rw9L34aIN3bZi98HXfSG0IjwwAbw9agBBrvK4whigZdXi1CtbhP/9tofEpBS74yilVG5pCOw1xuw3xlwBZgKd023zKDDaGHMGwBiT0ezkB4CfjTGXcjWtUvmJMVbP36Pr4f4voGTN628fFAJ3DoenNkHP6VZR+vvr8GE1+G4wHN3gmtwJZ2B6dxAv6DUTAgq75rwewiN/RRARRrStSq9xq5m6+hCP3HWL3ZGUUio3hANH0nwfAzRKt01lABFZAXgDrxhjfkm3TU/gw3TL3hSRl4DfgJHGmMvpTy4ig4BBAOHh4cTFxd3odThNQkKC5tAcbpEjYMM4grbM5FKjp0ks0QQc581WhuK3w7234xW3l4DNk/Hf/i2yeQbJJSJJrPMQVyq2Bx//m8qXYY6UJArOG4DPmUNcuO8rkin8V+7c4i5/N7LLIwtjgNtvDeWuSmF8tmQfPRuWpYC/x16KUkrdDB+gEtAciACWikgtY8xZABEpBdQCFqTZ53ngOOAHjAX+DbyW/sDGmLGO9URGRhp3mPToLpMvNUc+z7FnEax4G6p3JqjtSwSl6ZSTowwhDaFiQ0h8CzbNwGftWAosfBpW/B/U729NiCtU6oYiZpjjp2fhyAroPJpCtdrf0HGdksONeeRQiquea1OFuItXmLD8gN1RlFIqNxwFyqT5PsKxLK0YYJ4xJskYcwDYjVUoX9Ud+M4Yk3R1gTHmT2O5DEzEGrKhlMqOU7utvr/Fa0CXMeCM9pEBhaHxYBgaBX2+gfB6sPQ9+KgmzO4Ph1ZZQzduxtpxsG483DEM6va5+cx5VJb/NUVkgoicFJHoTNYXFpEfRGSzY0b0AOfHzFidMkVoW6ME45bu58zFK646rVJKuco6oJKIVBARP6whEfPSbfM91t1iRCQMa2jF/jTrewEz0u7guIuMiAjQBcjw/V0plU7CWZjZy+o80Ws6+AU79/heXlaXiN5fw7AN0Ggw7PsdJraDL+6CDVPgyg1MFdj7G/z8b6jcHlq/4tzMeUx2fs2ZBLS7zvohwHZjTB2sN+cPHG/gLvFsmyrEX0nm86X7XHVKpZRyCWNMMjAUaxjEDmCWMWabiLwmIp0cmy0AYkVkO7AYq9tELICIlMe64/xHukNPE5GtwFYgDNBeTUplJTXFulN85hD0+AqKlM3d84XcAm3ftLpZ3Pux9RS9eU/CqOqw8L9w5mD2jnNqN8weAMWqQtdx4OWdq7E9XZYDc40xSx1vrpluAhR03HkoAMQBLmvOV7lEQe6LDGfyyoM83KQCJQrpo1uVUnmHMWY+MD/dspfSfG2AZxyv9PsexJrAl355S6cHVSqvW/QS7PvNKlLL3eG68/oFW+ON6z0Eh1bC2i9g1WhY+QlUaW89We+WFiAZPA34UpzVgcLHD3rPBP+CrsvtoZwxY+1TrI/2jgEFgR7GmAyfvJFbM5z731aceZuP8f7P0Tx/d846VLjLbEnN4V4ZNIfmUEqpv2yaDqs+tZ5cV7+/PRlEoHwT63XuKERNgPWTYNd8CKsMtz0Kkb3+Ln5TrsDXA+H8Uej/U+7f4c4jnFEYtwU2YTWQvxVYJCLLjDHn02+YWzOcQ0KgZ8NYZq49wpOtq1M2NCjb+7rLbEnN4V4ZNIfmUEopAI6ss/oVV2gKbd+yO42lcDi0+i80+xds+w7WfAE/j4DfXrOK44aDCFryPhxaDveNhTI6vza7nNGVYgDwrWOG817gAFDVCcfNkSdbVsLbS/jot92uPrVSSiml8qLzx6wn2xUqDd0mg7ev3Yn+yccf6vSEQYvhkd+hagfrLvKnDQjYNhPuehbq9LA7pUdxRmF8GGgFICIlgCr8c0a0S5QoFED/O8rz3caj7D5xwdWnV0oppVRekpQAM3vDlYvW456D3PyTqoj6cP9YeHobtHyRhAZPQIsX7U7lcbLTrm0GsAqoIiIxIjJQRAaLyGDHJq8DdzhmOP8G/NsYY8ODv2Fws1sJ9vPhw4V611gppZRSN8gYqwPEsU1w/zgoUd3uRNlXoDg0HUHCHSOc02M5n8lOV4peWaw/BrRxWqKbUDTYj0fvuoVRv+5m85Gz1ClTxO5ISimllPI0Kz6CrbOh5X+t4Qkq38hzv0oMvKsCIcF+vL9wl91RlFJKKeVpdv0Cv74KNe63xuiqfCXPFcYF/H14ovmtLNtzmtX7Y+2Oo5RSSilPcWoXfPMIlKoNnUdn3BtY5Wl5rjAG6NO4HCULBfD+gl2Ym322uFJKKaXyvktxMKMn+AZAz+ngl/3WryrvyJOFcYCvN8NaVSLq0BmW7DpldxyllFJKubOUZJgzAM4egR7ToHCE3YmUTfJkYQzQrUEE5UKDeG/BLlJT9a6xUkoppTKx8EXYvwQ6joKyjexOo2yUZwtjX28vnm5dme1/nmd+9J92x1FKKaWUO9rwFawZA40eh3p97U6jbJZnC2OAe+uUpkqJgny4cDfJKal2x1FKKaWUOzm8Gn58Gm5pDm3esDuNcgN5ujD29hKebVOZ/acv8u2Go3bHUUoppZS7OHsEvu4DRcrAAxPBO8tHO6h8IE8XxgB3Vy9BnTJF+OjX3VxOTrE7jlJKKaXsduWS9bjnpEToNdP9H/esXCbPF8Yiwr/aVuHYuUSmrzlsdxyllFJK2ckYmDsEjm+FB76EYlXsTqTcSJ4vjAGaVAzjjltDGb14LxcvJ9sdRymllFJ2WfYBbPsWWr8MldvanUa5mXxRGAM817YKp+OvMGnlQbujKKWUUsoOO3+C31+HWt2gyXC70yg3lG8K43pli9K6Wgk+/2Mf5y4l2R1HKaWyTUTaicguEdkrIiMz2aa7iGwXkW0iMj3N8hQR2eR4zUuzvIKIrHEc82sR8XPFtShlmxPb4dtBULoudPpEH/esMpRvCmOAZ9tUJv5yMl8s3Wd3FKWUyhYR8QZGA+2B6kAvEamebptKwPNAE2NMDSDtrbAEY0yk49UpzfJ3gFHGmIrAGWBgbl6HUra6FAcze4FfsPW4Z99AuxMpN5WvCuNqpQrRqU5pJq44yMkLiXbHUUqp7GgI7DXG7DfGXAFmAp3TbfMoMNoYcwbAGHPyegcUEQFaAnMciyYDXZyaWil3kZIEs/rB+WPW454LlbY7kXJj+a5p39OtK/Pjlj/5bPE+XulUw+44SimVlXDgSJrvY4D0z6ytDCAiKwBv4BVjzC+OdQEiEgUkA28bY74HQoGzxpjkNMcMT39iERkEDAIIDw8nLi7OOVd0ExISEjSH5shRjqAlLxNwcBnxd7/PleBbIZdzuvPPIj/nyK58VxiXDwume4MyTFtziEfuqkCQ3YGUUurm+QCVgOZABLBURGoZY84C5YwxR0XkFuB3EdkKnMvOQY0xY4GxAJGRkSYkxP5er3FxcWgOzZHtHFETYcsUuH0oBZo8ak8Gm2iOG5OvhlJcNaxVRUSEj3/dY3cUpZTKylGgTJrvIxzL0ooB5hljkowxB4DdWIUyxpijjj/3A0eS8MQAACAASURBVEuAukAsUEREfK5zTKU826GVMP85qNga7n7N7jTKQ+TLwrhU4UD6NS7HNxtiOBibYHccpZS6nnVAJUcXCT+gJzAv3TbfY90tRkTCsIZW7BeRoiLin2Z5E2C7McYAi4EHHPs/BMzN7QtRymXOHoav+0LR8tD1S/DytjuR8hBZFsYiMkFETopI9HW2ae5oBbRNRP5wbsTc8XjzWwn09ea93w+QnJJqdxyllMqQYxzwUGABsAOYZYzZJiKvicjVLhMLgFgR2Y5V8I4wxsQC1YAoEdnsWP62MWa7Y59/A8+IyF6sMcdfuu6qlMpFVy7CjN7WpLteMyGwiN2JlAfJzhjjScCnwJSMVopIEeAzoJ0x5rCIFHdevNwTWsCfkR2q8d/vo/nXnC28360OXl7a01Ap5X6MMfOB+emWvZTmawM843il3WYlUCuTY+7H6nihVN5hUuG7wXByG/SeBWGV7E6kPEyWhbExZqmIlL/OJr2Bb40xhx3bX7dNkDvp27gcf54+x2fLjxDo580bXWoi2vBbKaWUyr4LJ/A6exR8Llv9gX2DwNvXlgdoBKz9FHbMgzZvQKW7XX5+5fmc0ZWiMuArIkuAgsDHxpjM7i67Xeuf3pEhXEpKYdKaw3ibZJ5qVtaW4thd2pm4Qw53yKA5NIdSKhviT8LHdSiSnG6+jnhZBbJv4N/Fcto/fQKuXfbXn9dbFwg+gRkX4Dt+IGjNKKjTC24f6vqfhcoTnFEY+wD1gVZAILBKRFYbY3an39AdW/8AvNwlklQvX6asOkRY4QIMa+X6j17cpZ2JO+RwhwyaQ3MopbJh0zRITuBis1cJLlgYkhIg6ZLjzzRfJyf8vSzhTMbbpSZnfb70xPvvojnxLMklIvHp+JE+7lndMGcUxjFArDHmInBRRJYCdbDaBWXKK/44GOMWf3lFhFfurcHFyyl8uGg3QX7ePHLXLXbHUkoppdxXaiqsnwzlmnC5Tj+Cb/YX1pSkawvqf3x9CZIT//l92m28fLlQ+xGK+gY45/pUvuSMwngu8KmjH6Yf1hOZRmW1k9elU7DxK6jXzwkRbp6Xl/BO11okJCXzxk87CPb3oVfDsnbHUkoppdzTwaVw5gC0eME5x/P2tV4BhW74EEaHWamblGVhLCIzsPpjholIDPAy4AtgjPncGLNDRH4BtgCpwHhjTKat3a4yfgVg/ggoXRdKZjhp2uV8vL34qEddEq5E8cJ3Wwny86Zz5DVPSVVKKaXU+skQUASqdYILl+xOo5RTZKcrRa9sbPMe8F5OTpxaqAwEJsOsfjBoCQQUzsnuucbPx4sxferTf+Janpm1mQBfb9rWKGl3LKWUUsp9XDwNO36A2x6xJsuhhbHKG2x78p3x8oEHJsKZQzB3qDXe2E0E+Hoz/qHbqBVemCenb2TZnlN2R1JKKaXcx6bpkJoE9R+yO4lSTmXvI6HL3Q6tX7Z6Dq753NYo6RXw92HygIbcWrwAj06JYt1BHbeklFJKYQxsmAxlGkHxananUcqp7C2MAe4YBlU6wMIX4cg6u9P8Q+EgX74a2JDSRQJ5eOI6tsSctTuSUkopZa9DKyB2L9Tvb3cSpZzO/sJYBLp8BoVKw+z+cMm97syGFfBn2iONKBzkS78Ja9l1/ILdkZRSSin7rJ8E/oWhehe7kyjldPYXxgCBRaH7FLh4Er4dZPVGdCOlCgcy7ZFG+Pt40efLNRw4fdHuSEoppZTrXYqD7fOgdnfwC7I7jVJO5x6FMVht29r9H+xdBMs/sDvNNcqFBjPtkUakpBr6jF/D0bMJWe+klFJK5SWbZ0LKZZ10p/Is9ymMARoMhJoPwOK34MBSu9Nco2Lxgkx5uCHnE5N4cNxqTl5ItDuSUkop5RpXJ92FN3Cb5w8o5WzuVRiLwL0fQ2hFmDMQLhy3O9E1aoYXZtKA2zh54TJ9x6/lzMUrdkdSSimlct+RNXBqp94tVnmaexXGAP4FrPHGV+JhzsOQkmx3omvULxfC+H4NOBB7kYcmruVCYpLdkZRSSqnctX4S+BWEGvfbnUSpXON+hTFYfRE7jrJawix+w+40GbqjYhhjHqzH9mPnGTgpioQrKXZHUkoppXJHwhnY9h3U7mbdwFIqj3LPwhigTk+o9xAsHwW7frE7TYZaVSvBqB6RRB2KY9BXUVxO1uJYKeVcItJORHaJyF4RGZnJNt1FZLuIbBOR6Y5lkSKyyrFsi4j0SLP9JBE5ICKbHK9IV12P8lBbZkNyovXvslJ5mPsWxgDt37UG+H/3mPXoaDd0b53SvN21Nsv2nObJ6RtJSnGvVnNKKc8lIt7AaKA9UB3oJSLV021TCXgeaGKMqQEMd6y6BPRzLGsHfCQiRdLsOsIYE+l4bcrta1EezBhrGEWpSCitv0OpvM29C2PfAGu8sUm1Hv6RfNnuRBnq3qAMr9xbnYXbTzBi9mZSU43dkZRSeUNDYK8xZr8x5gowE+icbptHgdHGmDMAxpiTjj93G2P2OL4+BpwEirksuco7jq6Hk9v0SXcqX3Dvwhgg5BboPBqObbAeG+2m+jepwIi2Vfh+0zFenBuNMVocK6VuWjhwJM33MY5laVUGKovIChFZLSLt0h9ERBoCfsC+NIvfdAyxGCUi/s4OrvKQ9RPBNxhqPWB3EqVynY/dAbKleidoPARWj4ayjaFmV7sTZWhIi4pcvJzMZ0v2EeznzQsdqiEidsdSSuVtPkAloDkQASwVkVrGmLMAIlIK+Ap4yBhzdazX88BxrGJ5LPBv4LX0BxaRQcAggPDwcOLi4nL3SrIhISFBc7gyx+ULFN36DZerdOLSxSS4mPG58s3Pw0MyaI4b5xmFMcDdr0LMOpg3DErWhrBKdifK0Ii2Vbh0JYVxyw4Q7O/D8NaV7Y6klPJcR4Eyab6PcCxLKwZYY4xJAg6IyG6sQnmdiBQCfgL+Y4xZfXUHY8yfji8vi8hE4LmMTm6MGYtVOBMZGWlCQkKccEk3Jy4uDs3hwhzrvoPkBALueIyA65wn3/w8PCSD5rhx7j+U4ipvX+g2Ebz9YFY/uHLJ7kQZEhFe6lidbvUj+OjXPYxbut/uSEopz7UOqCQiFUTED+gJzEu3zfdYd4sRkTCsoRX7Hdt/B0wxxsxJu4PjLjJifaTVBYjOzYtQHsoYaxhFiVpQup7daZRyCc8pjAEKR0DXcXByB8zP8AaHW/DyEt7uWpt7apfizfk7mLbGPTtqKKXcmzEmGRgKLAB2ALOMMdtE5DUR6eTYbAEQKyLbgcVY3SZige5AU6B/Bm3ZponIVmArEAa4Z8N4Za9jG+H4VutJdzosUOUTnjOU4qqKraHpCFj6LpS9Her1tTtRhry9hFHdI0m4ksKL30cT5OfNfXUj7I6llPIwxpj5wPx0y15K87UBnnG80m4zFZiayTFbOj+pynM2TAafQKjd3e4kSrlMlneMRWSCiJwUket+1CYit4lIsojk/rTV5iOhQjPrrvHxrbl+uhvl5+PFZw/W4/ZbQnlu9hZ+iT5udySllFI3yxhISbI7Re66fAG2zoGa90NAYbvTKOUy2RlKMQmrOXymHE3o3wEWOiFT1ry8oet4CCgCsx6CxPMuOe2NCPD1Zly/BtSJKMyTMzbwx+5TdkdSSil1o84dhbHNKDTrfkhJtjtN7on+Bq7Ea+9ile9kWRgbY5YCWfXZeBL4BquBvGsUKG5NxjtzEOYNtX6Dd1PB/j5MHNCQSsUL8thXUazZH2t3JKWUUjl1bCOMawknd+JzKhqiJtidKPesnwzFq0PEbXYnUcqlbnqMsYiEA/cBLYDr/h/k9J6YBasScMdzBK14h4uLP+Ry5IAcH8KV/fU+6VqZR2Zs4+FJ6/i8R3VqlCpgS47rcYcc7pBBc2gOpf5h+zz4dhAEh8GgxST9OALfJW9ZD70I8pxWVNny52broVrt3tFJdyrfccbku4+AfxtjUrN6mEWu9MRsNRJObSF4+f8RXLkZRDTI0e6u7K8XEgIzBhWm2xcrGTpnJ18/1piqJQu5PMf1uEMOd8igOTSHUoD1aeSKj+DXVyC8AfScDgVLcOmuFyk84x744x1o/47dKZ1r/WTwCdBJdypfcka7tgbATBE5CDwAfCYiXZxw3Ozx8oL7xkChUtZ440vufSepZOEApj/SmEBfb/qMX8v+U/F2R1JKKZWR5CvWUL1fX4Ea90H/H6FgCQBSwqpa42/XjoNTu2yN6VRXLsLW2VC9S967E65UNtx0YWyMqWCMKW+MKQ/MAZ4wxnx/08lyIrAodJsMF09aH3Wlpma9j43KhAQx9ZFGGGPoM34NMWfc82ElSimVb12Kg6n3w8apVovQrhPAN/Cf27T4D/gVgAX/sSdjbtj2HVw+b/UuViofyk67thnAKqCKiMSIyEARGSwig3M/Xg6E14O2b8HeRbBilN1pslSxeAG+GtiI+MvJPDh+DacuXLE7klJKKYDYfTC+NRxZA/d9AS1ftD6dTC84DJr9y/p3Z88i1+fMDesnQVhl6zkBSuVD2elK0csYU8oY42uMiTDGfGmM+dwY83kG2/ZP/+hRl7rtEajZFX5/Aw4ssy1GdlUvXYhJDzfk9IXL9Jy8mQXbtM+xUkrZ6uByGN8KEs5Av3lQp+f1t284CEJuhQUveH5v4xPbIGadNUREJ92pfMqzHgmdFRG492PrTWrOw3DhhN2JslSvbFG+faIJJQr489hX63nm602cS/DwN1ellPJEG6fBlC4QXAwe/Q3KZeOuqY8ftH0TTu+GdV/mfsbctH4yePtB7Sx+GVAqD8tbhTGAf0HoPsV6as83Az2iAXuVkgWZ3Lcmw1pVYu7mY7QdtVQfBKKUUq6SmmpNsJv7BJS7AwYugpBbsr9/5XZwSwtY8n9uPwE8U0kJsGUmVOsEwaF2p1HKNnmvMAYoUR06joKDy2DJW3anyRZfby+eubsy3z1xBwUDfHhowlpe+G4r8Zfdv7BXSimPdeUSzH4Ilo+yhhD0+QYCi+TsGCLWHJfL563i2BNtnwuJ5/RJdyrfy5uFMUBkL6jXD5Z9ALtd86RqZ6gdUYQfnryTx5rewoy1h2n/8VJW65PylFLK+S4ch0kdYMcP0OZN6PgRePve2LFKVIcGD1vDKU7udG5OV1g/yRqGWP5Ou5MoZau8WxgDtH8XStaC7wbB2SN2p8m2AF9vnu9QjdmP3Y6XCL3Grea1H7aTmJRidzSllMobjm+1Hu98arf10I47ht78hLPmL4B/AWsinjHOyekKJ3fC4VVWizaddKfyubxdGPsGWv2NU1Ngdn+rWbsHaVA+hJ+fuou+jcsxYcUBOvxvGRsPn7E7llJKebZdP8OXba3i9eFfoGoH5xw3OBSajYR9v3lW+7YNU8DLF+r0tjuJUrbL24UxQOit0Hk0HI2CRf+1O02OBfn58Frnmkwd2IjEKyl0HbOS9xbs5HKy3j1WSqkcMQZWjYYZvSCsEjz6O5Sq7dxz3PYIhFb0nPZtSYmweTpUvQcKFLM7jVK2y/uFMUD1TtD4CVjzufVUHw90Z6Uwfnm6KV3rRTB68T46f7qC7cfO2x1LKaU8Q0oS/Pi0VbBWvQcGzIdCpZx/Hh8/ayJe7B5YN975x3e2HT9YPZt10p1SQH4pjAFavwoRt8HcJ+H0XrvT3JBCAb68160OXz7UgNiLV+g8ejmf/r6H5BT3fgS2UkrZKuEsTOsG6ydCk+HQ/SvwC86981VqA7e2tDpUXHTzydMbJkPR8lChmd1JlHIL+acw9vGDbpOsGcezH7J6NnqoVtVKsHB4U9rVLMX7C3fTdcxK9p6MtzuWUiqXiEg7EdklIntFZGQm23QXke0isk1EpqdZ/pCI7HG8HkqzvL6IbHUc838ieXTWVdwB+LKN1b6z06dw96sZP97Zmf5q3xbv3u3bTu+1fi71+uX+z0QpD5G//k8oHAH3j7Meezn/ObvT3JSiwX580qsun/auy+G4S9zzv2WMX7af1FQPmgmtlMqSiHgDo4H2QHWgl4hUT7dNJeB5oIkxpgYw3LE8BHgZaAQ0BF4WkaKO3cYAjwKVHK92uX81LnZ4tfV45/gT0Pd7qNfXdecuXs1q3xY1AU7ucN15c2LDJPDygcg+didRym3kr8IYoFJraPocbJxqPf7Tw3WsXZoFTzflrkrFeOOnHfQct5rDsZfsjqWUcp6GwF5jzH5jzBVgJtA53TaPAqONMWcAjDEnHcvbAouMMXGOdYuAdiJSCihkjFltjDHAFKCLKy7GZbbMgsn3QkBheOQ3qHCX6zO0cLRv++V592vflnwZNk2HKu2hYAm70yjlNnzsDmCL5s/DkTXw07N4dy8PIU3sTnRTihcMYFy/+nyz4SivzttGu4+X8kKHajzYqCzZ+nTUGDi0EqK/wSe8KYTkrX8flfJw4UDaRuwxWHeA06oMICIrAG/gFWPML5nsG+54xWSw/B9EZBAwCCA8PJy4OPsfd5yQkHD9HMYQuGYUgWs/ISm8EfEdxmC8ioKTs2eZw8G/4VMEL32NCxvmkFShlVMz5CRHen67f6TApVguVOpKkhN+Njeaw9ncIYc7ZNAcNy5/FsZe3tD1S/j8LgrN6mp9vNb4cQi5xe5kN0xEeKB+BHfcGsq/5mzhxe+jWbDtOO90rU3pIoEZ75SaCrt/huUfQcxaEC8KRX0Jfw6yJiv6Bbn2IpRSN8oHazhEcyACWCoitW72oMaYscBYgMjISBMSEnKzh7xpcXFxZJojKRHmPgHR30Dkg/h2/IiiPn6uz5FWs2GwbQYFV/wf1OlszXexI0d6u+ZA4bIUjOxk/ZtoVw4nc4cc7pBBc9y4/DeU4qoCxWHAfK5UugeiJsIn9eHrvnBknd3JbkrpIoF8NbAhr3epSdTBM7T9aCnfrI/BpP0YL/mK9RHaZ41hZm+IPw4d3ocR+0iMHAhrx8LYZnBso30XopS66ihQJs33EY5lacUA84wxScaYA8BurEI5s32POr6+3jE9S/xJmNzRKopbvWz1r8+lojhHvH2tiXhx+2DdOLvTWOL2w4E/HJPubr4oViovyb+FMUDorVy8+z0YvtVq4XPgD/iytfVEpB0/WE/M80AiQt/G5fhl+F1ULVmQZ2dvZtBX6zkVGwurPoP/1YXvH7cmXdw/Hp7cCA0fhaAQLjV9EfrNhSsXYXxr+OM9SEm2+5KUys/WAZVEpIKI+AE9gXnptvke624xIhKGNbRiP7AAaCMiRR2T7toAC4wxfwLnRaSxoxtFP2CuS64mN5zYDuNawfFo6D4F7nrGvR5tXLkNVGwNS96Bi6ftTmM96U68oO6DdidRyu3k78L4qkKloPXL8PR2aPcOXDgGX/eBTxtYDdqveOZktnKhwcwcdDuv312SOns/w/eT2rDgeShaDnrPhsdXQO1u4J1uRM0tza111bvA4jdgYnvrDoNSyuWMMcnAUKwidwcwyxizTUReE5FOjs0WALEish1YDIwwxsQaY+KA17GK63XAa45lAE8A44G9wD7gZ5ddlDPt+dVqx5Zy2XpoR/X08xLdRJs34Uo8LH7L3hwpSdbE88rtoFBpe7Mo5Yby5xjjzPgXgMaDrUd67vwBVn4CPz0Lv79pLWv4qDUEw1OcPYz3qtH0XT8ZvBJY7deYdy+0IyKgOa9F1KDI9e6oBBaFB760Ziz/9AyMuRPa/Z/10Zs73YlRKh8wxswH5qdb9lKarw3wjOOVft8JwIQMlkcBNZ0e1pXWjoOf/wXFa0DvmVZLTndVvKr178i6cXDbQChRw54cu36Giyf1SXdKZSLLO8YiMkFETopIdCbrHxSRLY5G8StFpI7zY7qYtw/UuM9q8TPgFyh7Oyx9D0bVhHlPwqlddie8vhPb4dvHrCET68ZDzfvhiTU0+PfPtGjdkflb/+TuUUv5feeJrI9V6wF4fCVE1IcfhjnGJJ/K/WtQSqnMpCTD/H9Z/egrtYGHf3Hvoviq5iPBv5C97dvWT4JC4dbQDqXUNbIzlGIS12/8fgBoZoyphfWR3Vgn5HIPIlDudug1HYZGWeOxtsyC0Q1hWnc4sMy9elMeXg3Te8CY260x0g0HwVOboctnULwqPt5ePNmqEnOHNiE02I+HJ0XxrzmbuZCYdP3jFo6AvnOtCSR7f7OOv8szP3VVSnm4yxdgRk9Y+wU0HgI9p1uf9nmCoBCrt/GBP+x5Dz1zCPb9DnX76qQ7pTKRZWFsjFkKZNqAzhiz8mpTeWA1/5zpnHeEVYSOo+DpbdD8BTi63poBPbY5bJ1jjduyQ2oq7PrFmjA4oS0cWWvlezraGvqQwV2UGqULM3doE4a0uJU562No99EyVuzNYkKIlxfcPgQGLYECJa1/mOYNsx55qpRSrnD+GIXmdLOKu46joN1bnlfgNXgYwqrAwv9YHYJcaeNX1g2fuvqkO6Uy4+wxxgO5zgQOj2wWfw0vqP0oVO+L/87vCNg4Hu9vBpKy8L9crjOAxBo9wL9g7udIScJvz48ErP8Cn9hdpBQsTWKzl7lcvTv4BkEikHj94w28rTgNwwN56ae9PDh+DT3qlWRAg7Drn9e3JHSdTeCaUQSsH0vqvj+Ib/MhKaXqZj97FtylGbjm0BzKzSz8L97nDkOfOXBrS7vT3Jir7dumdbXuet/xpGvOm5IMG76yhlAUKZP19krlU04rjEWkBVZhfGdm23hcs/isFB8Cdz4OexbgvfJTgpa/SdC6T6xJDY0GQ+FrHiR18zmuXLJ+61/5CZw7AsWqwX1f4F2zK8HevgTn8BKahYTwS+UI3luwiwkrDjB360keqB9B/zvKU7H4dQr8e9+FWp3x/m4whec8AHc9B83+Zb3p3yR3aQauOTSHciNnj8C270iM7E+gpxbFV1VqbY2N/uNdqNMLgrO4IeEMexZYPevrf5j751LKgzmlXZuI1MZq+9PZGBPrjGN6DC8vq3PDgJ/g0d+t38ZXjYaPa8O3g+DPLc45z6U4qwfmRzWtWdiFI6DX19bEuDo9b6ogDfTz5qV7q/PTsDtpUzWUWVExtP5wKX2/XMNvO06QmprJOOryTeDx5VC7Jyx912qZdHrPDedQSqlMrf0CgMt1+tubw1navAlJl+D3N1xzvvWTrWFwldq65nxKeaibLoxFpCzwLdDXGLP75iN5sPD60G0iDNtoTXzb+RN8cRdM7mT12ryRiXrnYuCXF6yOGEvegojbrE4ZD/8CVdpZhbmT1ChdmFfaV2TVyJaMaFuFPSfiGTg5ihYfLOHL5Qc4n9EkvYDCcN8Yq6n+mQPw+V1WJwx3mpSolPJsieetwq56Z1IL5ZFpLMUqw22PwobJ1oNJctPZI7B3kTW2OH3feqXUP2SnXdsMYBVQRURiRGSgiAwWkcGOTV4CQoHPRGSTiETlYl7PULScNfHt6W3Q+lU4vdsaTzbmDtg4FZIvZ32MU7vg+yfg4zqw5nOodi88vgp6f211yshFoQX8GdKiIsv+3YJPe9elWAF/Xv9xO43f+o2X5kaz92QGE+6qd4YnVlt3kX96FqZ1gwvHczWnUiqf2DgVLp+H24fancS5mv3LurmwIJfbt22cah2/Xt/cO4dSeUSWvzoaY3plsf4R4BGnJcpLAovAncOh8ROw7VtrXPDcIfDba9Yd5QYPW+170jqyDpaPgl0/gU+g1RD+9iFQpKzL4/t6e9Gxdmk61i7N1phzTFp5kJlrjzBl1SGaVi7GgDvK06xyMby8HA/8KFgSHpxj3TFe+F/47Ha492Oo3un6J1JKqcykJMPqMVY/+Yj6kJcmXgaFQIv/WP2Yd/4E1To6/xypKda8lFtbQtHyzj++UnmMPhLaFXz8rHHAg5dD3++gRE34/XUYVQPmj4C4/fgeXAIT74EvW8PhldBspHXHuf07thTF6dWKKMwH3euw8vmWPHt3ZXb+eZ4Bk9bR8oMlTFxx4O9eyCLWEwIfW2rlntXXuvOdeN7eC1BKeaadP8C5w3nvbvFV9QdAsaqw8MXsfZqYU3t/hfNHof5Dzj+2UnmQFsauJGL91t73Wxi8Aqp3gaiJ8L+6FJw3wBqj2/b/YHg0tHgegkPtTnyNsAL+PNmqEsv/3ZL/9apLSLAfr/5gDbN4Zd429p9yDLMoVhke+RWajoDNM+DzJnBopb3hlVKexRhY+SkUrWBNcs6LvH2s9m1nDljD5pxt/SQILg5VOjj/2ErlQToK3y4la1qT1lq9BJumEe9dmAKNHrLuLnsAPx8vOtUpTac6pdl85CyTVx5k2ppDTFp5kOZVitH/jvI0rVQMr5YvWm2Jvh0EEztYQ0uav+Ax16mUstGRtXA0Cjq873kP8siJiq2sbhF/vGe1bytQ3DnHPX8Mdi+AJsOc0kpTqfxA7xj/f3v3HR9Vlf5x/POkJ6STQio1NEkIRYoFkCaKYkXBij/bFndddldXt+iuq6vbXF1FVxcRVykKNkQEdwERERAioYQmndAhEHoJeX5/3AGGABJgMneSPO/Xa16ZuXPn3u8kcPLkzLnnuC02Dbr8ksMtbqq2xWLrrHievzWfGY91Z3DPphRt3M2gN+fQ8/lpvPX1GvamtHWGkbS90xk/PbQ7bF3idmxjTKCb+RJExEP+bW4nqXpXPgNlB3w7fdu8EaBHoe1dvjumMTWcFcbGZ1JiIni4Zw4zftWdFwfkExsZypPjiuj8p8n84fO1rLnkORgwCnZvgte6wsxXnCWtjTGmopJVsGS8c5Fy2LkuXVQNJeU4F2V/+x/fzH9fXu4cq2FXSGx04cczppawwtj4XFhIENflZ/DRjy/lwx9dQo8WKbwzay1X/P0L/m9WMjOvHI827uZMUfT29VC6we3IxphAM+tfEBTiFIu1RddHITIBJv36wqdvWzXFuWix3SCfRDOmtrDC2FSpNtkJvDCgDTN+1Z2fds9hQXEpA0etoufGHzKz5RNo8Vx4tTMsHOt2VGNMoDiw05l7N/dmcfI9fQAAIABJREFUZ7hZbRGZAFf8GtZMh6XjL+xYBcMhqi407+uTaMbUFlYYG79IiY1gcK+mzHjsCv5xa2vqRIQy8Nvm9D3yLMXBWfD+vfD+fc4vRGNM7VYwHI7sc+Zwr23a3QPJLS5s+rY9W2DZZ87Y7JBw3+Yzpoazwtj4VXhIMDe0yeTjH1/KBz+6hCbN8uix8zGeL7uZowvf59BLnQhZ+yUcOeB2VGOMG8oOw+zXnLGx9XLdTuN/wSHQ50+wc42zsMn5KBwB5WXQdpAvkxlTK9h0bcYVIkLb7ATaZiewpW8LRsxqyqBZ7fn93hdp/PHd8DFoSCQSlQiRiRCV4HzMGJnorBYVmeg8Pv78sW3xNXtaJ1PriEgf4EUgGBiqqs9VeH4Q8Ffg2GD9l1V1qIhcAfzDa9fmwABV/UhEhgNdgVLPc4NUtbDq3sU5KPoQ9myCa//pdhL3NO4OTa+CL//m9Pqey/Rt5eXw7VtQ/zJIalJ1GY2poawwNq5LjY3g572bcfCKJkz8ticfTf0PR/dsIU320y4CmkQfJuxwqTPF2/4SZ7iFHj3zASPivIrlMxXTCSdvC49xFmAxJoCISDAwBOgFFANzRGScqi6usOu7qnrS0nCqOhXI9xwnEVgBfO61yyOqGliD+1WdKdqSmkGTnm6ncVfvp+GVTs4qqf1eqvzrVk9zepuv+G2VRTOmJrPC2ASMiNBgru+Yw44mP2PZLmXo9NX8bulWwkOCuKldJvde1pDGydHOL8+DpU6BfKAE9nu+HtjpKZxLvL7ugO3fOc8d+p5lqYNCTy6aIxOJikyBjvdAWmv/fROMOVkHYIWqrgIQkdHAdUDFwvhsbgY+U9X9Ps7nW2umw+aFTm9xUC0f6ZfUBDo+CDOHwMX3Vb4d+vYtpw1rcW3V5jOmhrLC2AQcEeGSxnW5pHESK7bu4Y2vVjO2oJiRs9fRo3kK913eiE6NEpHIeKBh5Q989Agc2FWhcC7xKrBLThTaO9cQvuJ/UPgmZLRzLohpdROERVXZ+zbmNDKA9V6Pi4GOp9nvJhHpAiwHBqvq+grPDwCer7DtGRF5ApgMPKaqp1zpJSIPAA8AZGRkUFJScn7vopKip/2DkMi67MrqDWc414EDB6o8R2X4I4fk3U/cvJEcHf8Ie24cddpPtbxzyP7txC8Zz6G8O9m/Zz/gv7+DatPPpTpksBznzwpjE9CapMTw7I15/KJ3M96euZa3Z61l4L9n0Sojlvsvb8TVuWmEBleyZyk4FKKTnVsl7Nq0hoR1k2DuMBj3EEz6DbQeAO3vgZQW5/+mjPGtT4BRqnpIRB4E3gK6H3tSRNKAXGCS12seBzYDYcDrwK+ApyoeWFVf9zxPfn6+JiYmVtV7gG3LYc0U6PY4iSlnnqKtpKSEKs1RSf7JkQg9fkvQpz8ncctX0PK678+x5G0oP0LEJQ8S4efvUe36uQR+Bstx/mr5Z1WmukiKDmdwr6Z8/Vh3nr0xlwOHj/Lw6EK6/mUqr3+5kt0Hj/j8nBoe63yU+aNZcM9n0LQ3FLzpjPsbdhUseA+OHPT5eY3xsgHI8nqcyYmL7ABQ1R1evb1DgXYVjnEL8KGqHvF6zSZ1HALexBmy4a5ZQyA4HNrf63aSwNL2bkhp6Uzf9n3tjSoUvAXZnSGluf/yGVPDWGFsqpWI0GAGdsjmv4O7MmxQe+rXrcOfJizlkmen8MfxiyneWQUfHYpA/UvgpqHw86XQ64/OVfMf3A/Pe+Yb3bHS9+c1BuYAOSLSUETCcIZEjPPewdMjfEw/YEmFYwwERp3uNSIiwPXAIh/nPjf7tsP80c4nMpX8RKfWCA6BPs/CrnUw65Uz77fmKyhZ6RTSxpjzZkMpTLUUFCR0b55K9+apLNpQytDpq3jr6zUM/3oNfVrV4/7LG5GfFe/7E9epC5f+FDo/5Fz9PXcYzHwFvn4JGnWD9v8Hza52hm0Yc4FUtUxEHsIZBhEMDFPVIhF5CpirquOAn4pIP6AMKAEGHXu9iDTA6XGeVuHQI0QkGRCgEPhBFb+V7zfnDSg7WDsX9KiMRt2cdmX6353p22LqnbpPwXBnRp6LrvdzOGNqFiuMTbXXKiOOFwa04dE+zXnr6zWMnL2OTxds4uIGCdx3eSN6tkglOMjHU7EFBUHjK5zb7k3O8rUFw+G9uyA6FdrcCe3uhvhs357X1DqqOgGYUGHbE173H8cZM3y6167BuYCv4vbup+7tkiMHYc6/Iac3JDdzO03g6v00DOnoTN923ZCTn9tfAkvGORcJh0a6k8+YGsKGUpgaIz0+ksevbsHMX/fgd9e0ZOOugzz4dgE9/v4Fb89cw4HD3zP38YWITYOuj8DPFsBt70F6G6dn54U8GHELLJsI5VV0bmOqu4Xvwb5t1lt8NnUbO9c8zBsBGyusxTJ/FBw97Pwxboy5IGctjEVkmIhsFZHTjkETxz9FZIWILBCRtr6PaUzlRYeHcO9lDZn2SDdevq0NcVFh/O7jIjo/N5m/TVrG1j1VdMFcUDA0vRJue9cpkrv8EjbNh1G3OkXytL84vcvGGIeqM09vaq6zBLT5fl0fhai6MPFx53sHnovuhkPmxZB6kavxjKkJKtNjPBzo8z3PXwXkeG4PAOe5uLsxvhUSHMQ1eel89KNLGPODznRokMiQL1Zw2XNTeWTMfJZt3lN1J4/Phu6/hcGL4Ja3ISkHpj4D/7gI3r0DVk5xlm4NZOXlUFrsXNQzbwQh62dYz7fxrRWTYdtSp7fYVp48u4g46P4bWPc1LP4IgJBNc2H7cmg3yN1sxtQQZx1jrKpfei7gOJPrgP+oqgKzRCReRNJU1brGTEAQES5ukMjFDRJZvX0fw75azZiC9YwpKKZL02Tuv7whlzVJQqriF3NwKLTs59x2rHR6dua9A0s+gYSGzpzI+bdDnSTfn7syDu2FXWuhZLWzjKz3bdda5+NZj1iAyRmQdwu0HmjjQc2Fm/kyRNdzFs8xldP2budixc+fgKZXEb5oFITHwkU3uJ3MmBrBFxffnW5lpgzglMLY36soVUagrMhiOfyTIS4IBndJ556LkxlbuIX3vt3MnW98Q05yFHdcnEafFknHFwzxeQ5JgPaDIf9HhK2cSPjCkYT+9wl08tMcbtKHQ7m3UZbe4ZSeswvKoeXIvq0El64lqHQ9wbvXEVS6juBS52vQgR0n7V4eFkN5XH3KE5pwtEF3ymOzORqXRXlsFkc3zCNm5XhCZ/wT+eoflKXkcajFDRzOuRaNqnu+35VzFgj/Ro0PbF4Eq6ZCjycgJMztNNVHUDBc+Sf4Tz+Y+jRh302ANndAWB23kxlTI/h1Vgq/rqJUSYGyIovl8G+GRODRjFQevvIiPi7cyBvTV/PkhJUMmV7M3Zc04PaO2URC1eVIuQc63wNblyIFbxJeOIrw5eMgqZkz5VvrWyEyAajE9+PwPti51qu317v3dy0c9VrpV4IgLhMSGkB6X6fXOqHB8VtQZAJBZ+g5L4lvQNjl98LebbBoLCGFIwmZ9gfqTH8Gcq505qBteiWEhPvkW3QmgfBv1PjAzCEQGuXMpGDOTaOu0Pwa+PolBGwYhTE+5IvC+KwrMxkTqMJDgrmlfRb922Xy5XfbGTp9FX+dtIyXp6zgmlZJ3N8tlKapMVUXIKU5XPVn6PEkFH0Ac9+Eib+C//0eWt3oFMmRDZyL9k5b+K6BvVtOPmZYDCQ2gOTm0LTPSYUv8dkXPsdydDJ0+qFz21LkLMyw4D1Y9ilExDsfi+ffBhntbNyoOb09m2HhGKegi7I/cs5L7z/C8kmUJbUgJC3P7TTG1Bi+KIzHAQ+JyGigI1Bq44tNdSMidG2aTNemySzdvJuh01fzUeEGxszbQocGidzeKZs+reoRHhJcNQHCopyPQ9vc4cxkMfdNp9gsHEFCUBiUnxjriwRBbCYk1Hfmfj1W9CY2dHqAIxP8V5CmXuT8gu75e+dj8fmjoXAkzH0D6jZxepHzbrX5nM3JvnkdysucP67M+UlsBLe/x77ySOLczmJMDXLWwlhERgHdgCQRKQaeBEIBVPVfOBPPXw2sAPYD9rmYqdaa14vlb/1b88PO9Zi8ai8jZq/j4dGFJNYJo3/7TG7rkE39ulU4ni+tNVz7AvR6ChaN5eDGxUSmNfcUwA0hLivwxmQGBUOTns7t4G5nsYHCUTDlaefW4HLngr2W/SC8CnvgTeA7vM9ZMbJ5X2duXnP+GnfnqI23N8anKjMrxcCzPK+AzcxuapyEqFAe6NKY+y5rxIyV2xkxax1Dp6/mtWmruDwnids71qdnixRCgqtonZyIWGj/fxwoKSGyOo2pjYg90fu9c63T8z1/FHz8I/j0F9DiWqcnuVE3p6A2tUvhSDiw01lW3RhjAowtCW3MWQQFCZfnJHN5TjKbSw/y7pz1jJ6zjh+8U0BqbDgDLs5mQIcs0uJsKdZTJNR3VgXs8ksonuMUyIved1Y7i0k7MfVbSgu3kxp/KC+HWa8448+zO7mdxhhjTmGFsTHnoF5cBA/3zOHHVzRm6rJtjJi9ln9O+Y6XpnxHjxap3N4xmy45yQQF2UVnJxGBrA7O7cpn4btJzlCLmUNgxouQlu8UyLk3uzens6l6yz+DklVw8zC7MNMYE5CsMDbmPIQEB9GrZSq9Wqaybsd+Rs1Zx3tz1vPfxVvISozktg716d8+k6Toqp26rFoKjYCW1zm3vducHuT5o5zZOD7/DTTp5Qy1aHZVlU/9Zvxs5hBnjHyL69xOYowxp2WFsTEXKLtuFL/q05zBPZsyqWgz78xay58nLuX5/y6jT6s07uiYTYeGiVWzsl51F50MnX7g3LYshgWeqd+Wf+aZ+u1Gpyc582LrYazuNnwLa2dA72cg2H71GGMCk7VOxvhIWEgQ17ZO59rW6azYuocRs9fxfkExn8zfSJOUaG7vmM2NbTOJi7zAeYRrqtSWzkwcPZ6E1dOcoRaFo5wZDBIbOwVy3i2AzWpRLc0c4syx3fYut5MYY8wZWWFsTBVokhLDk9dexKNXNmf8go28M3sdf/hkMX+euJRr89K5vVN9WmfGWS/y6QQFQ+Puzu3QHlg8zhlqMfVpmPo0CUGePyyOf+/k3B+f8hxn3vd7H5tK2bUeij505i2OiHU7jTHGnJEVxsZUociwYPq3z6J/+ywWbShlxOx1fFy4gTEFxbTKiOX2jvXp1zqdOuH2X/G0wmOgze3Obdc6KPqIgzs3ERnhGXus6tlRz/L4XPatxGNV4MXzf1+1zTevOV87PuhuDmOMOQv7bWyMn7TKiOPZG3P59dXN+ahwIyNmreXxDxbyzKdLuKFNBrd3yqZ5PetNO6P4bLj0pwE0r7MVxpVycDcUvOVcbGkrIBpjAlwVrUxgjDmTmIhQ7uxUn88evpz3f9iZ3i1TeXfuevq8MJ2bX/2aD+cVc/DIUbdjmgAiIn1EZJmIrBCRx07z/CAR2SYihZ7bfV7PHfXaPs5re0MRme055rsiUjXLKc57Bw7ttgU9jDHVghXGxrhERGhXP5Hnb81n1uM9+M3VLdi+9xCD351Pp2cn88yni1m9fZ/bMY3LRCQYGAJcBbQEBopIy9Ps+q6q5ntuQ722H/Da3s9r+5+Bf6hqE2AncK/Pwx8tg1mvQnZnyGzn88MbY4yvWWFsTABIrBPG/V0aMeUX3RhxX0cuaVyXN2es4Yq/fcGDo4v4cF4xBw5bL3It1QFYoaqrVPUwMBq4oImAxbnqszsw1rPpLeD6C0p5Oks/gdJ11ltsjKk2bIyxMQEkKEi4tEkSlzZJYutuz/LT36xl8Lvz+V14EdfkpdG/fSZtsxNsRovaIwNY7/W4GOh4mv1uEpEuwHJgsKoee02EiMwFyoDnVPUjoC6wS1XLvI6ZUfGAIvIA8ABARkYGJSUllU+tSuyXLyBx9SlN7gjn8trvceDAgXPLUUUsh+UI5AyW4/xZYWxMgEqJjeAnPXIYmJ/Id7tgbEExHxduZPSc9TRKqsNN7TK5qW0m9eIi3I5q3PcJMEpVD4nIgzg9wN09z9VX1Q0i0giYIiILgdLKHFRVXwdeB8jPz9fEc7nocd1s2FIIV/+NxKTkc3gr36+kpIRzylFFLIflCOQMluP82VAKYwJckAidG9fl77e0Zs5ve/KXm/NIig7nr5OWcclzk7lr2Dd8Mn+jXbBXc20AsrweZ3q2HaeqO1T1kOfhUKCd13MbPF9XAV8AbYAdQLyIHOscOeWYF2zmS87qhfm3+fSwxhhTlazH2JhqJDo8hFvaZ3FL+yzWbN/H+98W835BMT8ZNY/YiBD65afTv10WebZ4SE0yB8gRkYY4xesA4KRqU0TSVHWT52E/YIlnewKw39OTnARcCvxFVVVEpgI344xZvhv42GeJS1bBkvFw2WAIq+OzwxpjTFWzwtiYaqpBUh1+0bsZP+vZlJkrdzCmYD1j5hbzzqx1NE2N5uZ2mVzfJoOUGBtqUZ2papmIPARMAoKBYapaJCJPAXNVdRzwUxHphzOOuAQY5Hl5C+A1ESnH+YTwOVVd7HnuV8BoEXkamAe84bPQs/4FQSHQ4QGfHdIYY/zBCmNjqrngIOGynCQuy0li98EjjJ+/ibEF6/nThKX8eeIyujVNpn/7TLo3TyUsxEZPVUeqOgGYUGHbE173HwceP83rvgZyz3DMVTgzXvjWgZ3O3MW5N0Nsms8Pb4wxVckKY2NqkNiIUG7rmM1tHbNZsXUvYwuK+eDbYiYv3UpCVCjX5WfQv30mF6XHuR3V1FQFw+HIPuj8Y7eTGGPMObPC2JgaqklKNI9d1Zxf9m7K9BXbGVtQzMjZ6xj+9RpapsUeH2qRWKdqFjwztVDZYZj9GjTsCvVO21FtjDEBrVKFsYj0AV7EGd82VFWfq/B8Ns70QPGefR7zfPRnjHFZSHAQVzRL4YpmKezaf5hP5m9kTEExT41fzLOfLaF78xT6t8uia7NkQoNtqIW5AEUfwp5N0O8lt5MYY8x5OWth7LUcaS+cSeDniMg4rws4AH4LvKeqr3qWKp0ANKiCvMaYCxAfFcadnRtwZ+cGLNu8h7EF6/lw3gYmFW0hKTqcG9qk0799Fk1TY9yOaqobVWeKtqRm0LiH22mMMea8VKbH+PhypAAicmw5Uu/CWIFYz/04YKMvQxpjfK9ZvRh+07clj/ZpzrRl2xhTsJ43Z6zh39NXk5cZR/92mVzbOp34KBtqYSphzXTYvBCu/ScE2ScPxpjqqTKFcWWWI/098LmI/ASoA/Q83YEuaHnRKhIoSxVajsDKUNtytK0XStu+jdh5RRafLd7OuEVb+d3HRTw1fjHdmiTSLzeZ1qnhVZqhsgLl52Iq+PpliEqCvFvdTmKMMefNVxffDQSGq+rfRaQz8LaItFLVcu+dLmh50SoSKEsVWo7AylBbcyQmwkOZqTzU+yKKNpYyZm4xHxdu4L/LdlC3TihX56bTNy+NixskEhzkzgIigfJzMV62LYfvJkG3xyHU5s02xlRflSmMz7ocKXAv0AdAVWeKSASQBGz1RUhjjP9dlB7HRf3i+PXVLZiydAvvz1nL2IJi3p61luSYcK5uVY++eem0r59AkEtFsgkQs4ZAcDi0v9ftJMYYc0EqUxifdTlSYB3QAxguIi2ACGCbL4MaY9wRFhJEn1ZpdEgPJyI6lilLt/Lpgk2MnrOet2auJTU2nKtapXFt6zTaZFmRXOvs2w7zR0PrARCd7HYaY4y5IGctjCu5HOkvgH+LyGCcC/EGqapWZXBjjP9FhYVwTV461+Sls+9QGZOXbuXTBRsZ+Y0zP3JaXARX56bRNy+NNlnxiFiRXOPNeQPKDtqCHsaYGqFSY4wrsRzpYuBS30YzxgSyOuEh9GudTr/W6ew9VMbkJVsYv2ATb89cyxtfrSYjPpKrc53hFq0z46xIromOHIQ5/4ac3pDczO00xhhzwWzlO2PMBYsOD+G6/Ayuy89g98EjTF6yhU8XbGL41870bxnxkVyT5/Qk52ZYkVxjLHwP9m2z3mJjTI1hhbExxqdiI0K5oU0mN7TJpPTAEf63eAufLtzEsBmree3LVWQlRtI3N51r8tK4KD3WiuTqShVmDoHUXGcJaGOMqQGsMDbGVJm4yFBuapfJTe0yKd1/hM8Xb+bThZsYOn0V/5q2kvp1o+jrGZPcMs2K5GplxWTYthSu/xfYz80YU0NYYWyM8Yu4qFD6t8+if/ssdu0/zOdFW/hkwUZe+3IVr3yxkoZJdY4Xyc3rxViRHOhmvgzR9aDVTW4nMcYYn7HC2Bjjd/FRYdxycRa3XJxFyb7DTCrazKcLNvHKFyt4eeoKGiXX4ZrcNPrmpdOsXozbcU1FmxfBqqnQ4wkIsSXDjTE1hxXGxhhXJdYJY2CHbAZ2yGbH3kNM9BTJL09dwT+nrCAnJZq+eWn0zU0jJ9WK5IAwcwiERkG7e9xOYowxPmWFsTEmYNSNDuf2jvW5vWN9tu05ViRv5MXJ3/HC/76jaWo07TKj6dBkP7kZcTRMinZtaepaa89mWDgG2g2CKFua2xhTs1hhbIwJSMkx4dzZqT53dqrP1j0HmbhoM58t3MyHC7YyqmAzAHXCgrkoI468jDhyM+PIy4ynfmJUjVp9T0T6AC/iLLA0VFWfq/D8IOCvOCuTArysqkNFJB94FYgFjgLPqOq7ntcMB7oCpZ7XDFLVwkoF+uZ1KC+DTj+8kLdljDEByQpjY0zAS4mJ4K7ODbircwO2bt/BzrIwFhTvYuGGUhZuKOU/s9ZyuKwcgJiIEHKPFcoZ8eRlxpGZEFktL+YTkWBgCNALKAbmiMg4z6JK3t5V1YcqbNsP3KWq34lIOlAgIpNUdZfn+UdUdew5BTq8D+YOg+Z9oW7jc39DxhgT4KwwNsZUKyFBQrN6MTSrF0P/9lkAHDlazndb9rJwwy4WFDvF8rCvVnPkqLMyfXxUKLkZceRlxpHrKZbT4iKqQ7HcAVihqqsARGQ0cB1QsTA+haou97q/UUS2AsnArjO/6iwKR8KBndC5Yg1ujDE1gxXGxphqLzQ4iJbpsbRMj+XWi51th8qOsnzzXhZs2MXC4lIWFJfyr2mrOFruFMtJ0WGenuV48jxFc0pshIvv4rQygPVej4uBjqfZ7yYR6QIsBwarqvdrEJEOQBiw0mvzMyLyBDAZeExVD1U8qIg8ADwAkJGRwdEZL6Oprdkd3RRKSi7kfZ23AwcOUOLSuS2H5aguGSzH+bPC2BhTI4WHBJOb6QypOFZKHjxylCWbdrNwg1MoLywuZdry7/DUyqTGhh/vUc7NjCM3I46k6HD33kTlfAKMUtVDIvIg8BbQ/diTIpIGvA3crarlns2PA5txiuXXgV8BT1U8sKq+7nmeti0aaXDpGug1jMS6davw7Xy/kpISEhPdv+jPcliOQM5gOc6fFcbGmFojIjSYNtkJtMlOOL5t/+EyFm/cfXwIxoLiXUxeugX1FMsZ8ZEnxix7iuX4KL/N3bsByPJ6nMmJi+wAUNUdXg+HAn859kBEYoFPgd+o6iyv12zy3D0kIm8CvzxbENm/HeKyocV15/wmjDGmurDC2BhTq0WFhdC+QSLtG5zo0dhz8AhFG3c7QzA2lLKweBcTizYffz47Mcpf8eYAOSLSEKcgHgDc5r2DiKR5Fbr9gCWe7WHAh8B/Kl5kd+w14gyyvh5YdLYgcmQfdPwBBNuvDWNMzWUtnDHGVBATEUqnRnXp1OjEkIHS/UdYtNEzBGPDLqb7IYeqlonIQ8AknOnahqlqkYg8BcxV1XHAT0WkH1AGlACDPC+/BegC1PVM6QYnpmUbISLJgACFwA/OmiUsBtre5bs3Z4wxAcgKY2OMqYS4qFAubZLEpU2SAHj1Dv+cV1UnABMqbHvC6/7jOGOGK77uHeCdMxyz++m2f5+j8Q0gIvZcX2aMMdVKkNsBjDHGGGOMCQRWGBtjjDHGGIMVxsYYY4wxxgCVLIxFpI+ILBORFSLy2Bn2uUVEFotIkYiM9G1MY4wxxhhjqtZZL74TkWBgCNALZ9WlOSIyTlUXe+2Tg3Pxx6WqulNEUqoqsDHGGGOMMVWhMj3GHYAVqrpKVQ8Do4GKM7zfDwxR1Z0AqrrVtzGNMcYYY4ypWpWZri0DWO/1uJjjC6we1xRARGbgzLX5e1WdWPFAIvIA8ABARkZGQKydHShreFuOwMpgOSyHMcaY2sdX8xiHADlAN5wlS78UkVxV3eW9k6q+DrwOkJ+fr4GwdnagrOFtOQIrg+WwHMYYY2qfyhTGG4Asr8eZnm3eioHZqnoEWC0iy3EK5TlnOuj8+fP3isiyc8xbFZKA7W6HwHIEWgawHBVZjpM1czuAP1mbfQrLcTLLEVgZwHJUVKk2uzKF8RwgR0Qa4hTEA4DbKuzzETAQeFNEknCGVqw6y3GXqWr7yoSsSiIy13IEVo5AyGA5LEdlcridwc+szbYclqOaZLAcp89Rmf3OevGdqpYBDwGTgCXAe6paJCJPiUg/z26TgB0ishiYCjyiqjvOL7oxxhhjjDH+V6kxxqo6AZhQYdsTXvcV+LnnZowxxhhjTLXj5sp3r7t4bm+W42SBkCMQMoDlqMhynCxQcvhLoLxfy3Eyy3GyQMgRCBnAclRUqRzidPYaY4wxxhhTu7nZY2yMMcYYY0zAsMLYGGOMMcYYXCqMRaSPiCwTkRUi8phLGYaJyFYRWeTG+T0ZskRkqogsFpEiEXnYpRwRIvKNiMz35PiDGzm88gSLyDwRGe9ihjUislBECt2clktE4kVkrIgsFZElItLZhQzNPN+HY7fdIvIzF3IM9vz7XCQio0Qkwt8ZPDke9mQocuP74AZrs0/KYe32qVmszT6Rw9rsEzmqZ5utqn694SwZvRJoBIQB84GWLuToArQFFvn73F4Z0oC2nvsxwHKXvhcCRHvuhwKzgU4ufl9+DowExruYYQ2Q5Nb5vXKeJ2ouAAADtUlEQVS8BdznuR8GxLucJxjYDNT383kzgNVApOfxe8AgF95/K2AREIUzq8//gCZu/zvxw8/c2uwTOazdPjWLtdknclibrdW7zXajx7gDsEJVV6nqYWA0cJ2/Q6jql0CJv89bIcMmVf3Wc38PzjzRGS7kUFXd63kY6rm5clWmiGQCfYGhbpw/kIhIHE4x8AaAqh7WCsusu6AHsFJV17pw7hAgUkRCcBq5jS5kaIGzyud+deZ4nwbc6EIOf7I2++Qc1m57sTb7BGuzT1Et22w3CuMMYL3X42JcaFQCjYg0ANrg/NXvxvmDRaQQ2Ar8V1VdyQG8ADwKlLt0/mMU+FxECkTkAZcyNAS24awoOU9EhopIHZeyHDMAGOXvk6rqBuBvwDpgE1Cqqp/7OwdOz8PlIlJXRKKAq4EsF3L4k7XZZ2DtNmBttjdrsz2qc5ttF98FABGJBt4Hfqaqu93IoKpHVTUfyAQ6iEgrf2cQkWuArapa4O9zn8ZlqtoWuAr4sYh0cSFDCM5Hx6+qahtgH+DK+E4AEQkD+gFjXDh3Ak4vZUMgHagjInf4O4eqLgH+DHwOTAQKgaP+zmHcZ+22tdmnYW32iXNX2zbbjcJ4AydX65mebbWSiITiNK4jVPUDt/N4PvaZCvRx4fSXAv1EZA3Ox7XdReQdF3Ic+2sXVd0KfIjzcbK/FQPFXr1AY3EaXbdcBXyrqltcOHdPYLWqblPVI8AHwCUu5EBV31DVdqraBdiJM8a0JrM2uwJrt4+zNvtk1mafUG3bbDcK4zlAjog09Pw1MwAY50IO14mI4IxFWqKqz7uYI1lE4j33I4FewFJ/51DVx1U1U1Ub4Py7mKKqfv8LU0TqiEjMsftAb5yPY/xKVTcD60WkmWdTD2Cxv3N4GYgLH8l5rAM6iUiU5/9ND5yxnX4nIimer9k4Y9VGupHDj6zN9mLt9gnWZp/M2uyTVNs2O8QfobypapmIPARMwrlacpiqFvk7h4iMAroBSSJSDDypqm/4OcalwJ3AQs84MYBfq+oEP+dIA94SkWCcP5beU1XXpt0JAKnAh87/ZUKAkao60aUsPwFGeAqSVcA9boTw/LLpBTzoxvlVdbaIjAW+BcqAebi3zOj7IlIXOAL8OAAurqlS1mafwtrtwGNtdgXWZp/knNpsWxLaGGOMMcYY7OI7Y4wxxhhjACuMjTHGGGOMAawwNsYYY4wxBrDC2BhjjDHGGMAKY2OMMcYYYwArjI0xxhhjjAGsMDbGGGOMMQaA/wfLp99hjNcQrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y, splits = get_UCR_data('LSST', split_data=False)\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "cbs = [ShowGraph()] \n",
    "learn = TSClassifier(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms, arch=TSiTPlus, arch_config={}, metrics=accuracy, cbs=cbs)\n",
    "learn.fit_one_cycle(10, 1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now define which elements we'd like to test. These will be part of the config. \n",
    "\n",
    "Later we'll be able to modify the training script or the config."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.078094</td>\n",
       "      <td>1.588653</td>\n",
       "      <td>0.523114</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.612723</td>\n",
       "      <td>1.249077</td>\n",
       "      <td>0.600162</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.351654</td>\n",
       "      <td>1.232383</td>\n",
       "      <td>0.592052</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.199912</td>\n",
       "      <td>1.073855</td>\n",
       "      <td>0.655312</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.083166</td>\n",
       "      <td>1.046569</td>\n",
       "      <td>0.675182</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.974308</td>\n",
       "      <td>0.995019</td>\n",
       "      <td>0.678021</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.868292</td>\n",
       "      <td>0.999589</td>\n",
       "      <td>0.682887</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.781063</td>\n",
       "      <td>0.928972</td>\n",
       "      <td>0.702757</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.716529</td>\n",
       "      <td>0.922481</td>\n",
       "      <td>0.703569</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.657766</td>\n",
       "      <td>0.915174</td>\n",
       "      <td>0.706407</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "config = AttrDict (\n",
    "    batch_tfms = TSStandardize(by_sample=True),\n",
    "    arch_config = {},\n",
    "    architecture = TSiTPlus,\n",
    "    lr = 1e-3,\n",
    "    n_epoch = 10,   \n",
    ")\n",
    "\n",
    "X, y, splits = get_UCR_data('LSST', split_data=False)\n",
    "tfms = [None, TSClassification()]\n",
    "cbs = [ShowGraph()] \n",
    "learn = TSClassifier(X, y, splits=splits, tfms=tfms, batch_tfms=config[\"batch_tfms\"], arch=config[\"architecture\"], \n",
    "                     arch_config=config[\"arch_config\"], metrics=accuracy, cbs=cbs)\n",
    "learn.fit_one_cycle(config[\"n_epoch\"], config[\"lr\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform experiments with W&B 🛫"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just need to add 2 elements to have a configurable training script that can be tracked by W&B: \n",
    "\n",
    "- A context manager: \n",
    "\n",
    "    ```\n",
    "    with wandb.init(project=\"LSST_v01\", reinit=True, config=config):\n",
    "    ```\n",
    "    It may useful to pass a group, job type, tags, name, notes, etc. You can see the available options [here](https://docs.wandb.ai/ref/python/init).\n",
    "- A callback: \n",
    "\n",
    "    ```\n",
    "    WandbCallback()\n",
    "    ```\n",
    "\n",
    "\n",
    "⚠️ If you also want to save your best models, and set log_model=True in the WandbCallback, you'll need to add the SaveModelCallback as well.\n",
    "\n",
    "There's currently a small bug in the integration between wandb and tsai that doesn't allow to log_preds. This can be used to show predictions in W&B. We recommend setting log_preds=False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                Tracking run with wandb version 0.12.2<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">baseline</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/26yuu835\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/26yuu835</a><br/>\n",
       "                Run data is saved locally in <code>/content/wandb/run-20210922_061934-26yuu835</code><br/><br/>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.114535</td>\n",
       "      <td>1.589791</td>\n",
       "      <td>0.504461</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.632138</td>\n",
       "      <td>1.326617</td>\n",
       "      <td>0.592457</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.366500</td>\n",
       "      <td>1.315255</td>\n",
       "      <td>0.592052</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.198177</td>\n",
       "      <td>1.086082</td>\n",
       "      <td>0.643958</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.060263</td>\n",
       "      <td>1.011896</td>\n",
       "      <td>0.671533</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.946860</td>\n",
       "      <td>0.994727</td>\n",
       "      <td>0.680860</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.848480</td>\n",
       "      <td>0.985425</td>\n",
       "      <td>0.697080</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.757913</td>\n",
       "      <td>0.919544</td>\n",
       "      <td>0.710462</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.679247</td>\n",
       "      <td>0.905018</td>\n",
       "      <td>0.714517</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.623348</td>\n",
       "      <td>0.902409</td>\n",
       "      <td>0.714112</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br/>Waiting for W&B process to finish, PID 2392<br/>Program ended successfully."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb5b6854cd474c9888535587c87a9a3b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find user logs for this run at: <code>/content/wandb/run-20210922_061934-26yuu835/logs/debug.log</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find internal logs for this run at: <code>/content/wandb/run-20210922_061934-26yuu835/logs/debug-internal.log</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>0.71411</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.6759</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.62335</td></tr><tr><td>valid_loss</td><td>0.90241</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>▁▄▄▆▇▇▇███</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▇▆▅▅▄▄▃▄▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▂▂▁▁▂▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>█▇▇▆▆▅▅▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>valid_loss</td><td>█▅▅▃▂▂▂▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">baseline</strong>: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/26yuu835\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/26yuu835</a><br/>\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOU CAN MODIFY YOUR CONFIG AND/OR TRAINING SCRIPT IN THIS CELL AND RE-RUN MANUAL EXPERIMENTS THAT WILL BE TRACKED BY W&B\n",
    "\n",
    "config = AttrDict (\n",
    "    batch_tfms = TSStandardize(by_sample=True),\n",
    "    arch = TSiTPlus,\n",
    "    arch_config = {},\n",
    "    lr = 1e-3,\n",
    "    n_epoch = 10,   \n",
    ")\n",
    "\n",
    "with wandb.init(project=\"LSST_v01\", config=config, name='baseline'):\n",
    "    X, y, splits = get_UCR_data('LSST', split_data=False)\n",
    "    tfms = [None, TSClassification()]\n",
    "    cbs = [ShowGraph(), WandbCallback(log_preds=False, log_model=False, dataset_name='LSST')] \n",
    "    learn = TSClassifier(X, y, splits=splits, tfms=tfms, batch_tfms=config.batch_tfms, arch=config.arch, \n",
    "                         arch_config=config.arch_config, metrics=accuracy, cbs=cbs)\n",
    "    learn.fit_one_cycle(config.n_epoch, config.lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize results 🕸"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will be able to see your experiment results in the W&B website.\n",
    "\n",
    "The links are displayed with the run details like this: \n",
    "\n",
    "```\n",
    "Project page: https://wandb.ai/timeseriesai/LSST_v01\n",
    "Run page: https://wandb.ai/timeseriesai/LSST_v01/runs/34lacjyd\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to test multiple values: use loops? 🌀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even run loops. In this case we'll test if adding a convolution/s with different kernel sizes improves performance. You'll be able to check progress in the W&B website during the test. That's why are remove the ShowGraph callback."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                Tracking run with wandb version 0.12.2<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">vibrant-mountain-39</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/2hayjz4k\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/2hayjz4k</a><br/>\n",
       "                Run data is saved locally in <code>/content/wandb/run-20210922_061957-2hayjz4k</code><br/><br/>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.106912</td>\n",
       "      <td>1.585571</td>\n",
       "      <td>0.504866</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.616565</td>\n",
       "      <td>1.295642</td>\n",
       "      <td>0.560827</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.351119</td>\n",
       "      <td>1.182734</td>\n",
       "      <td>0.610300</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.188972</td>\n",
       "      <td>1.201490</td>\n",
       "      <td>0.617194</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.083488</td>\n",
       "      <td>1.102667</td>\n",
       "      <td>0.635848</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.984776</td>\n",
       "      <td>1.052097</td>\n",
       "      <td>0.656934</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.882159</td>\n",
       "      <td>1.037492</td>\n",
       "      <td>0.672749</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.791272</td>\n",
       "      <td>0.957581</td>\n",
       "      <td>0.692214</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.719182</td>\n",
       "      <td>0.919564</td>\n",
       "      <td>0.706407</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.664114</td>\n",
       "      <td>0.919914</td>\n",
       "      <td>0.708435</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
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     },
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     "output_type": "display_data"
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
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       "<br/>Waiting for W&B process to finish, PID 2428<br/>Program ended successfully."
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    {
     "data": {
      "text/html": [
       "Find user logs for this run at: <code>/content/wandb/run-20210922_061957-2hayjz4k/logs/debug.log</code>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "Find internal logs for this run at: <code>/content/wandb/run-20210922_061957-2hayjz4k/logs/debug-internal.log</code>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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      "text/html": [
       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>0.70843</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.51405</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.66411</td></tr><tr><td>valid_loss</td><td>0.91991</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr></table>"
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    {
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      "text/html": [
       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>▁▃▅▅▆▆▇▇██</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▇▆▅▆▃▄▄▄▃▃▃▃▃▂▃▂▂▂▃▂▂▂▂▂▂▂▃▂▂▂▁▂▁▁▂▁▂▁▁</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>█▇▇▆▆▅▅▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁</td></tr><tr><td>valid_loss</td><td>█▅▄▄▃▂▂▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
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     "data": {
      "text/html": [
       "Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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     "data": {
      "text/html": [
       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">vibrant-mountain-39</strong>: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/2hayjz4k\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/2hayjz4k</a><br/>\n",
       "                "
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     "data": {
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       "\n",
       "                Tracking run with wandb version 0.12.2<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">cosmic-feather-40</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/2rmci04e\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/2rmci04e</a><br/>\n",
       "                Run data is saved locally in <code>/content/wandb/run-20210922_062021-2rmci04e</code><br/><br/>\n",
       "            "
      ],
      "text/plain": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.022336</td>\n",
       "      <td>1.529024</td>\n",
       "      <td>0.551906</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.544441</td>\n",
       "      <td>1.251528</td>\n",
       "      <td>0.596107</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.289575</td>\n",
       "      <td>1.127850</td>\n",
       "      <td>0.639092</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.146650</td>\n",
       "      <td>1.042489</td>\n",
       "      <td>0.644769</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.016892</td>\n",
       "      <td>1.053039</td>\n",
       "      <td>0.661395</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.918026</td>\n",
       "      <td>0.971166</td>\n",
       "      <td>0.685726</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.822939</td>\n",
       "      <td>0.915165</td>\n",
       "      <td>0.708029</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.729064</td>\n",
       "      <td>0.912994</td>\n",
       "      <td>0.706407</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.655014</td>\n",
       "      <td>0.896970</td>\n",
       "      <td>0.717356</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.595882</td>\n",
       "      <td>0.897511</td>\n",
       "      <td>0.716545</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jZkBfYJuIbHYs+xdQDsAY8wXWdKX3Avuxhmvrl9UAfZqUZ9yqg3y6eA8zBzW9mexKqXwqODiYZs2aUatWLfz8/K41kwBo164dX3zxBdWrV6dq1ao0adLExqQqN9SsWZO4uDhCQkIoXbo0vXv3pkOHDtSuXZuGDRtSrVq1G+7/xBNP0K9fP6pXr0716tVp0KBBLiVXykWkpMDhVdaoErt+tEZyKFUb2n8EtbuAf/55cJmVUSl+A27YiNAxGsWQ7ATw8/HkyZaVeOvHnazZf4bbKxfLzmGUUvnMjBkzMlxeoEABFixYkOG6w4cPA1CsWDG2b99+bfmLL77o9Hwqd23b9lfHv2LFihEZGZnhdhcvXgSsEUpSfwb8/PyYOXNmzodUytWcP2Z1ots8zepUV6AI1OsD9fpCmQi709nCtpnv0urZqBzjVh7kk8V7aVopWKfdVEoppZTKCUlXYe8Cq+3wgaVgUiDsTqvtcPUO9g+rZjOXKIx9vT0ZcldlXvthOyv2RtOyagm7IymllFJK3ZyEC3jv+xmKFoMCAVCgsDWebwHHy9PGsuv0bqupxJaZcPkMBJSBO56Her0hqKJ9uVyMSxTGAN0aluXz5QcYvngvLaoU16fGSimVBxhj8s392mo1qNR1nN4N3/QmIGb/9bfx9k9XMGdQPGe23Nsfsvpv7kqcNTXzpqkQtd6aVrlqe6j3sDXcmoenc67djbhMYezj5cHQ1pV55bttLN11mrtrlMx8J6WUUrbx9fUlJiaG4GD3bwJnjCEmJgZfX1+7oyhXtGMO/DAEfAoS12ECAaUqwpUL1hTFV+LSvL/wz+UXTvz1/urFzM/l4eUonAOsNsEZFtIBFDyxC/b9DImXoFhVaPsu1OkBhYrn/N9HHuYyhTHAg/VDGbP8AJ8u3kuraiXw8HDvG61SSuVloaGhREVFER0dnWvnTElJwcOm4aJ8fX0JDXXPsVtVNiUnwdI3Yc3/ILQRdJtCYlIByO6EQCnJ6QrpTIrq1PcXjkHCrr+WpyTh4+0PtR6C+g9D6G1Zf8qcz7lUYezt6cEzrcN5ftYWFu08Sbtape2OpJRS6jq8vb2pUKFCrp7TVWaCVIpLZ+DbR61hzm4bAPf815oB7lamj/fwBL+i1iu7jIHEeM6eO09QCa2jbpbLjdLcKSKEisULMnzxPlJStD2XUurWFSpUCIDjx4/TpUuXDLdp2bIlGzZsyM1YSqm86thGGNvCarf7wOdw3yeuMy2yCPj4g1eBzLdV/+ByhbGnh/Ds3VXYcyqOn7ZlOnmeUkplWZkyZZg9e7bdMZRSednGyTCxHYgHPLbQmh5ZuQ2XK4wB7q9dmqolAxixZC9JySl2x1FKuZhhw4YxevToa5/ffPNN3n33XVq3bk39+vWpXbs2c+fO/cd+hw8fplatWgDEx8fTo0cPqlevTufOnYmPj8+1/EqpPCjpCswbCj8OhfLN4PEV+XYSDHfmUm2MU3l4CM+1CWfwtD+Yt+U4D9bXzg5KuaQFw+Dktsy3uxmlakP792+4Sffu3Xn22WcZMsSacHPWrFksXLiQoUOHUrhwYc6cOUOTJk3o2LHjdUdL+Pzzz/H392fXrl1s3bqV+vXrO/c6nEhE2gEjAU9gvDHm/XTrhwN3OT76AyWMMUUd6x4BXnOse9cYM9mxvAEwCfAD5gPPGB2PTKmMnY+Cb/rC8T+ssX9bvaZDnbkplyyMAdrWKEWN0oUZuXQfHeqWwdvTJR9uK6VsUK9ePU6fPs3x48eJjo4mMDCQUqVK8dxzz7Fy5Uo8PDw4duwYp06dolSpUhkeY+XKlQwdOhSAOnXqUKdOndy8hCwTEU9gNNAGiALWi8g8Y8zO1G2MMc+l2f5poJ7jfRDwBtAQMMBGx75ngc+BgcDvWIVxOyDjubSVys8OrYRv+1lPjLtPs2aHU27LZQtjDw/h+TZVGDBlA9//EUX328rZHUkplV4mT3ZzUteuXZk9ezYnT56ke/fuTJ8+nejoaDZu3Ii3tzdhYWEkJCTYls+JGgH7jTEHAURkJtAJ2Hmd7XtiFcMA9wCLjTGxjn0XA+1EZDlQ2Biz1rF8CvAAWhgr9RdjrGHYlrwBwZWh+3QoXsXuVCqHuWxhDNC6egnqli3KZ0v307leKD5e+tRYKWXp3r07AwcO5MyZM6xYsYJZs2ZRokQJvL29WbZsGUeOHLnh/s2bN2fGjBm0atWK7du3s3Xr1lxKftNCgKNpPkcBjTPaUETKAxWAX2+wb4jjFZXB8vTHGwQMAggJCSH2VoahcpL4+HjNoTlyPsfVSxRc8jIF9s/naqV2XGzzEXgWytJQbG73d+EmObLKpQtjEeup8SMT1zFrw1H6NClvdySllIuoWbMmcXFxhISEULp0aXr37k2HDh2oXbs2DRs2pFq1ajfc/4knnqBfv35Ur16d6tWr06BBg1xKnqN6ALONMcnOOJgxZhwwDiAiIsK4wvjBrjKOseZw4xxn9sN3veHMXrj7LXyaPUPQTUyO4VZ/F26UI6tcujAGaB5ejAblAxn16366NAjF11sbuyulLNu2/dXxr1ixYkRGRma43cWL1jSrYWFhbN++HQA/Pz9mzpyZ8yFv3TGgbJrPoY5lGekBDEm3b8t0+y53LA9Nt/x6x1Qq/9g9H+Y8bk273HcOVGxpdyKVy1y+bYKI8EKbKpy8kMDX6/60O45SSuW29UC4iFQQER+s4nde+o1EpBoQCKT97WAh0FZEAkUkEGgLLDTGnAAuiEgTsYbteBj45/h2SuUXKcnw67swsycEVbSGYqvY0u5UygYuXxgD3F65GE0qBjFm+QHirzrlG0KllMoTjDFJwFNYRe4uYJYxZoeIvC0iHdNs2gOYmXbINUenu3ewiuv1wNupHfGAJ4HxwH7gANrxTuVXl2NhRjdY+RHU62NN2lFUO/znVy7flCLV822q0m1sJNPWHmFg84p2x1EqXzPGXHd8YHfjCkP7GmPmYw2plnbZ6+k+v3mdfScCEzNYvgGo5byUSuVBJ7bCN33gwnG4fwQ0eNSaUlnlW3niiTFAowpB3BlejM9XHODSlSS74yiVb/n6+hITE+MSBWNOM8YQExODr6+v3VGUUs625RuY0AaSE6HfAmjYT4tilfkTYxGZCNwPnDbG/OPpgogUAaYB5RzH+9gY85WzgwI836YKncesYdKawwy5q3JOnEIplYnQ0FCioqKIjo625fwpKSl4eOTe7/S+vr6Ehursm0q5jaSrsOg1WDcWyt8BXb+CQiXsTqVcRFaaUkwCRgFTrrN+CLDTGNNBRIoDe0RkujHmqpMyXlOvXCCtqpVg3MqDPNy0PAG+3s4+hVIqE97e3lSoUMG28+e1oX+UUi4k7iTMegSOroWmT8Hdb4Kn1hLqL5k+djHGrARuNDKzAQIcPZsLObbNsbYOz91dhfPxiUz87XBOnUIppZRS7ubPtTC2OZzcCg9NgHve06JY/YMzOt+Nwho66DgQAHQ3xqRktKEzZlEK8Ye7wgP5ctUBOtUoQmHfW7sEV5mRRXO4VgbNoTmUUm7CGFj3JSx81Rptou8cKFnT7lTKRTmjML4H2Ay0AioBi0VklTHmQvoNnTWL0sv31qL9yFXM3naWF++pmv3kuM7XsprDtTJoDs2hlHIDVy/DT8/B1plQpR10Hgt+Re1OpVyYM3qw9AO+N5b9wCHgxnOx3qLqpQtzX53SfLX6ELGXnN6UWSmllFJ5XewhmNAWtn4DLf8FPb7WolhlyhmF8Z9AawARKQlUBQ464bg39GzrcC4nJjN25YGcPpVSSiml8pJ9S2BcSzj/J/SaBS1fgVwczUblXZn+lIjI11hTjFYVkSgR6S8ig0VksGOTd4DbRWQbsBR4xRhzJuciW8JLBtCpbhmmrDlCdNyVnD6dUkoppVydSbFmsJveBQqHwKDlUKWt3alUHpJpG2NjTM9M1h8HbPmpe+buKvy49QRfrDjAf+6vYUcEpZRSSrmChPMU+nkwHFwMtbtCh5HgU9DuVCqPydPfK1QoVpAH64Uwbe0RTl1IsDuOUkoppexwZj982QrvQ79Cu/fhwS+1KFbZkqcLY4ChrcNJTjGMXrbf7ihKKaWUym2HV8P41hB/jrgHp0OTJ3RqZ5Vteb4wLhvkT9eGZZm57ijHzsXbHUcppZRSuWXLTJjSyZrSecASkkIa251I5XF5vjAGeKpVZQBG/apPjZVSSim3Zwws+y/MeRzKNYH+iyDIvqnqlftwi8I4pKgfPRqV5dsNR/kz5rLdcZRSSimVU5KuWAXxivchojf0+R78Au1OpdyEWxTGAEPuqoynh/DZr/vsjqKUUkqpnHA5FqZ2tibtaPUadBoNXj52p1JuxG0K45KFfenTpDzf/xHFoTOX7I6jlFJKKWeKOQAT2kDUenhoAjR/STvZKadzm8IYYHCLShTw8mTkkr12R1FKKaWUs/y5FsbfbT0xfnge1O5idyLlptyqMC4eUICHby/P3C3H2Xcqzu44SimllLpV22bD5A5WO+IBS6B8U7sTKTfmVoUxwOPNK+Hv7cmIJdrWWCmllMqzjLGmd/6uP4Q0tIri4Ep2p1Juzu0K46CCPjx2RwV+3naCXScu2B1HKaWUUjcr6SrMHQK/vgu1u8HDP4B/kN2pVD7gdoUxwIA7KhLg68XwxdrWWCmVt4lIOxHZIyL7RWTYdbbpJiI7RWSHiMxwLLtLRDaneSWIyAOOdZNE5FCadRG5eU1K3VD8WZj2IGyeDi2GwYPjwKuA3alUPuFld4CcUMTfmwF3VGT4kr1sizpP7dAidkdSSqmbJiKewGigDRAFrBeRecaYnWm2CQdeBZoZY86KSAkAY8wyIMKxTRCwH1iU5vAvGWNm586VKJVFsYdgRjfrz85joW4PuxOpfMYtnxgDPHZHGEX9vfl08R67oyilVHY1AvYbYw4aY64CM4FO6bYZCIw2xpwFMMaczuA4XYAFxhidAUm5rqPrrZEnLp62mk5oUaxs4JZPjAECfL0Z1LwiH/6yhz/+PEv9cjorjlIqzwkBjqb5HAU0TrdNFQARWQ14Am8aY35Jt00P4NN0y94TkdeBpcAwY8yV9CcXkUHAIICQkBBiY2Ozex1OEx8frzncMIf3vp8ptOgFUgqWJK7TRFIKV4JsHM8V/j5cIYPmyD63LYwBHmkaxoRVhxi+eC9T+6f/f4lSSrkFLyAcaAmEAitFpLYx5hyAiJQGagML0+zzKnAS8AHGAa8Ab6c/sDFmnGM9ERERJijI/s5PsbGxaA43ymEMrB4BS96Eso3x7DGDogWL5X4OJ3KFDJoj+9y2KQVAwQJeDG5RiVX7zrDuUN75bUUppRyOAWXTfA51LEsrCphnjEk0xhwC9mIVyqm6AXOMMYmpC4wxJ4zlCvAVVpMNpXJXciL8+IxVFNd80Jq44xaKYqWcwa0LY4A+TcpTPKCAtjVWSuVF64FwEakgIj5YTSLmpdvmB6ynxYhIMaymFQfTrO8JfJ12B8dTZEREgAeA7TkRXqnrSjgP07vCH5PhzhetKZ69fe1OpZT7F8Z+Pp4MaVmJtQdjWbP/jN1xlFIqy4wxScBTWM0gdgGzjDE7RORtEeno2GwhECMiO4FlWKNNxACISBjWE+cV6Q49XUS2AduAYsC7OX0tSl1z7k+YcA8cXgWdRkPr/4CH25cjKo/ItI2xiEwE7gdOG2NqXWeblsAIwBs4Y4xp4cyQt6pHo3KMXXmQTxbvpWmlYKyHJEop5fqMMfOB+emWvZ7mvQGed7zS73sYqwNf+uWtnB5Uqaw4thFm9ICkK9DnO6jY0u5ESv1NVn5FmwS0u95KESkKjAE6GmNqAl2dE815fL09GXJXZTYeOcuKvdF2x1FKKaXyn10/wlf3WU0m+i/Soli5pEwLY2PMSuBGPdd6Ad8bY/50bJ/RGJq269awLKGBfgxfvBfrAYtSSimlcpwxsGYUfNMXStaEAUuhRDW7UymVIWcM11YF8BaR5UAAMNIYMyWjDe0eE/OxxmV4+5cD/LD+AC0qW0OHuMr4eprDtTJoDs2hlHKC5CRY8DJsmAA1Olmz2Xn72Z1KqetyRmHsBTQAWgN+QKSIrDXG7E2/od1jYva9syiT15/gy8gTdLA+7GoAACAASURBVGpYCQ8PcZnx9TSHa2XQHJpDKXWLEi7A7H6wfwk0exZav6Gd7JTLc8ZPaBSw0BhzyRhzBlgJ1HXCcZ3O29ODZ1qHs/PEBRbuOGl3HKWUUso9nY+Cr9rDgWXQYSS0eUuLYpUnOOOndC5wh4h4iYg/1nSlu5xw3BzRKSKESsULMnzJXlJStK2xUkop5VTHN8OXreHsEej9LTR41O5ESmVZpoWxiHwNRAJVRSRKRPqLyGARGQxgjNkF/AJsBdYB440xLjtYvKeH8OzdVdh76iI/bTthdxyllFLKfexZYD0p9vS2Rp6o3NruRErdlEzbGBtjemZhm4+Aj5ySKBfcV7s0o37dz4gle2nySG274yillFJ539ovYOGrULou9JwJAaXsTqTUTcuXDX48PITn2oRzMPoSv+zS2fCUUkqpbEtJxn/Fm/DLK1D1Xnj0Zy2KVZ6VLwtjgHtqlqJWSGE+/fUwe0/F2R1HKaWUynsuHIeve+C7ZTI0fQq6TQGfgnanUirbbCuMJSnBrlNb5xdhVM/6eHkIvcf/zqEzl2zNo5RSSuUZSVdg1Sfwv4ZwcAWXWr4D97wHHp52J1PqlthWGHuePQindtp1egDCihXki+41SE4x9P5yLVFnL9uaRymllHJ5e36B0Y1h6dtQ6S54ah1X6vSxO5VSTmFfUwrxgOldrK9hbFSxmD9T+zfi4pUkeo//nVMX7H2SrZRSSrmkM/thelf4urs16kSf76HHdAgMszuZUk5jW2GcXDQMEs5b/8gSLtgVA4CaZYow+bFGnIm7Qu/xvxNz8YqteZRSSimXcSUOFr8OY5rAkUho+x48sUaHYlNuybbC2Hj5Wo30o3fDrIchOdGuKADUKxfIhEdvI+rsZfpMWMf5y/bmUUoppWxlDGz5xmpHvHok1OkOT2+E25+ynhgr5YbsHZWicmvo8BkcXAbzhlr/CG3UpGIwY/s25MDpizzy1TouXkmyNY9SSilli+ObYeI9MGcQFC4DA5bCA6MhoKTdyZTKUfYP11avN7R8FbbMgOX/tTsNLaoUZ1Svemw7dp7HJq0n/mqy3ZGUUkqp3HEpBn58Bsa1hNiD0Gm0VRSHNrQ7mVK5wv7CGKDFK1CvD6z4AP6YYnca2tYsxafd6rL+cCyDpm7gSpIWx0oppdxYchL8Pg7+Vw82TYMmT1rNJur1AQ/XKBWUyg2ZTgmdK0Tg/hFw4QT8+CwElIHwu22N1CkihCuJKbz83VaemrGJMb3r4+2pNwellFJu5tAqWPAKnN4BFVtCuw+gRDW7UyllC9ep9Dy9odtkKFkDvn0ETmyxOxHdbivL251qsnjnKZ6ftYXkFHvbQCullFJOc+4ofPsoTL4frsZB92nQ9wctilW+5hpPjFMVCIBe38KENtYwbgOWQNFytkZ6uGkYl68m8/6C3fh5e/D+g3Xw8BBbMymllFLZlpgAaz6DVZ8CBlr+C5oNBW8/u5MpZTvXeWKcqnBp6P2t9Q93WheIP2t3Iga3qMTQ1uHM2hDFWz/uwNg8eoZSKn8RkXYiskdE9ovIsOts001EdorIDhGZkWZ5sohsdrzmpVleQUR+dxzzGxHxyY1rUTYyBnb9BKMbwbL3oEpbeGo9tHxFi2KlHFyvMAYoUd2aTefsIZjZ25qT3WbP3R3OwDsrMDnyCO//sluLY6VUrhART2A00B6oAfQUkRrptgkHXgWaGWNqAs+mWR1vjIlwvDqmWf4BMNwYUxk4C/TPyetQNoveC9MehG96g7c/PDzPmkvA5m9llXI1rlkYA1S4Ex74HI6shjmDISXF1jgiwr/urU6fJuUYu+Igny3db2sepVS+0QjYb4w5aIy5CswEOqXbZiAw2hhzFsAYc/pGBxQRAVoBsx2LJgMPODW1cg0JF2Dhv+HzpnBsI7T/EAb/BhVb2J1MKZfkWm2M06vdBc5HwZI3oEgotH3H1jgiwtsdaxF/NYXhS/bi7+PJwOYVbc2klHJ7IcDRNJ+jgMbptqkCICKrAU/gTWPML451viKyAUgC3jfG/AAEA+eMMUlpjhmS/sQiMggYBBASEkJsbKxzrugWxMfHa46s5DAp+Oz6Hv81HyCXY7hSszvxTV/E+AfD+Qu5lyOXuUIOV8igObLPtQtjgGbPwPmjVkeBouWg0UBb43h4CB88VJuExGTem78LXx9P+jYpb2smpVS+5wWEAy2BUGCliNQ2xpwDyhtjjolIReBXEdkGnM/KQY0x44BxABERESYoKChHwt+M2NhYNEcmOY5thPkvw7ENENoI+szGt0w9fHM7hw1cIYcrZNAc2ZdpUwoRmSgip0Vkeybb3SYiSSLSxXnxsMY4bv8hVL0XFrwMu3926uGzw8vTg+HdI7i7egn+88N2Zm+MsjuSUsp9HQPKpvkc6liWVhQwzxiTaIw5BOzFKpQxxhxz/HkQWA7UA2KAoiLidYNjqrzm4mmYOwS+bGU9UOo8Fh5bCGXq2Z1MqTwjK22MJwHtbrSBo3PIB8AiJ2T6Jw9PeGiC9Y97dn+I2pAjp7kZPl4ejOpVnzvDi/Hy7C38tPW43ZGUUu5pPRDuGEXCB+gBzEu3zQ9YT4sRkWJYTSsOikigiBRIs7wZsNNYvYeXAakPMh4B5ub0hagckpwIkWPgfw1gyzdw+1Br1rq6PXTWOqVuUqb/YowxK4HMGoc8DXwH3LDDxy3x8Yee30BASZjRDWIO5NipssrX25OxfRvQoHwgz87czJKdp+yOpJRyM452wE8BC4FdwCxjzA4ReVtEUkeZWAjEiMhOrIL3JWNMDFAd2CAiWxzL3zfG7HTs8wrwvIjsx2pzPCH3rko5i9fR1fDFHbDwVQi9DZ6MtPrjFAiwO5pSedIttzEWkRCgM3AXcFsm295iRw5PPDpMpPCshzBTHuRC19lWR4Jb4IxG4Z90qswT3+zkiekbGflQNZqEFbUlhzO4Qg5XyKA5NIcrMcbMB+anW/Z6mvcGeN7xSrvNGqD2dY55EGvEC5VXGGM1kTi+GU5shj9/p/CR3yAwDHrOhCrtrOaHSqlsc0bnuxHAK8aYFMnkH6RTOnIEBUHvWTC5A4ELBsMjP1pPk7PJGY3Cg4Dpg4rSY9xanp+zhymPNaZRhZs7pqs0TneFHK6QQXNoDqVsZYw1KtOJzXB801/F8OUYa72HFxSvzuWmL+Hf6kXwzsmudUrlH84ojBsCMx1FcTHgXhFJcgwJlDPKNoKHxsM3feG7AdB9qtUO2UZF/X2YNqAx3cZG8tik9Uwb0JiIsjf/5FgppVQ+YwxcOPb3Avj4pr+KYPGEEjWgansoHQFl6kPJmuDtS0JsLP5aFCvlNLdcGBtjKqS+F5FJwE85WhSnqt4B2n9gjVTxyzBr5Aqbv0IqVqgAMwY0oevYNTwycR0zBzWheunCtmZSSinlQq4VwZv//jT48hlrvXhas79WaQ9lIqxO5yVr6pTNSuWSTAtjEfkaq7dzMRGJAt4AvAGMMV/kaLrMNH4czv0JkaOgSFloNtTWOAClivgyY0ATuo2NpM/43/nm8aZULlHI7lhKKaVymzFw4fg/m0NcirbWiycUr2a1DS4TYT0NLlVLi2ClbJRpYWyM6ZnVgxljHr2lNNnR5h3rt+/F/4EiIVDroVyPkF7ZIH+mD2hMt7Fr6T1+Ld8+fjvlgrPfDloppZSLMwbiTljF7/FNfxXD14pgDyheHcLbOppDOJ4E30IfGaWU87n+zHeZ8fCAB76AuFMwZzAUKgVhzexORcXihZg+oDHdx0XSa/xaZj3elDJF9SmAUkq5A7l4Ck6vdRTAqUWwY8RS8bCeBFduk6Y5RC0tgpXKA/J+YQxWb9we02HiPTCzJ/RfDMWr2p2KqqUCmPpYY3p9uZY+439n5uNNKBGgnSSUUipPmzuEwE3TrPfiAcWqQuXWVgGc2hzCp6C9GZVS2eI+U+L4B0Hv2eBZAKZ1gbiTdicCoHZoEb7qdxsnzifQd/w6zl66anckpZRS2bVzLmyaRkKtntZ0y69GwZC10PkLq99LucZaFCuVh7lPYQwQWN4a4/hyjDU73pU4uxMB0DAsiPGPNORQzCUenriOCwmJdkdSSil1sy7Hws8vQKk6XG7xFpRrokWwUm7GvQpjsL7K6joJTm6Hbx+15pB3Ac0qF+OLPvXZffIC/b5az6UrSXZHUkopdTMWvALxZ+GBMeDpbXcapVQOcL/CGKBKW7j/U9i/BH56zuot7AJaVSvJyB712PTnWQZO2UBCYrLdkZRSSmXFngWwbRbc+QKUynCWbaWUG3DPwhigwaPQ/CXYNBVWfmR3mmvurV2aj7vWJfJgDE9M28jVpBS7IymllLqR+HPWQ5YSNeHOF+1Oo5TKQe5bGAPc9W+o2xOWvQebZ9id5poH64fy3gO1WbYnmmdmbiIpWYtjpZRyWYv+DRdPwwOjwcvH7jRKqRzk3oWxCHT4DCq2hHlPw4Ff7U50Ta/G5fjP/TVYsP0kL83eSoqLNPdQSimVxv4lsGmaNbNqmXp2p1FK5TD3LozB+u2+2xRrnMlvHoaT2+xOdE3/OyrwYtsqzNl0jGe+283hM5fsjqSUUipVwgWY9wwUqwIthtmdRimVC9y/MAbwLQK9v4UCATC9K5yPsjvRNU+1CueNDjXYdPQCbYev5MNfduuIFUop5QqWvAEXjkGn0dZEUkopt5c/CmOAIiHQZzZcvWQVx/Hn7E50Tb9mFZgzsB731y3NmOUHaPXJcuZuPobR5hVKKWWPgytgw0RoOgTKNrI7jVIql+SfwhigZE3oPhXO7INv+kCS68xCV7yQD592i+C7J26nRIAvz8zcTNcvItl+7Lzd0ZRSKn+5esnqlxJU0erErZTKN/JXYQxWR7xOo+DwKpg7xGXGOE7VoHwgc4c044OHanPozCU6jPqNf83ZRqxOJa2UUrlj6dtw7gh0HAU+/nanUUrlIi+7A9iibg+rnfGv7+CfDNz3XyhYzO5U13h4CN1vK0e7WqX5bOk+Jq05zE9bjvNC26r0blwOL8/89/uMUkrliiOR8PtYaDQIwprZnUYplcvyb4V15wtw+1AK7JoNI+rAkrfgcqzdqf6miJ83/7m/Br88cyd1Qovyxrwd3PfZb6zZf8buaEqpXCIi7URkj4jsF5EMh0YQkW4islNEdojIDMeyCBGJdCzbKiLd02w/SUQOichmxysit67HpSXGw7ynoGhZaP2G3WmUUjbIv4WxCLR9h/O9F0HV9vDbcKtA/vU9iD9rd7q/CS8ZwNT+jRjbtwGXribRa/zvPDl9I1FnL9sdTSmVg0TEExgNtAdqAD1FpEa6bcKBV4FmxpiawLOOVZeBhx3L2gEjRKRoml1fMsZEOF6bc/pa8oRl/wcx+6Hj/6BAIbvTKKVskH8LY4eUoErQZQI8GQnhd8PKD2FEXVj+PiS4Tsc3EeGemqVY8nwLXmhThV93n6b1JysYsWQvCYnJdsdTSuWMRsB+Y8xBY8xVYCbQKd02A4HRxpizAMaY044/9xpj9jneHwdOA8VzLXleE7UBIkdB/UesvihKqXwp3xfG15SoDl0nweDVULE5LP8vjKgNKz6yBnl3Eb7enjzdOpylL7SkTY2SjFiyj9afrGDBthM6vJtS7icEOJrmc5RjWVpVgCoislpE1opIu/QHEZFGgA9wIM3i9xxNLIaLSAFnB89Tkq5YnbEDSkPbd+xOo5SyUaad70RkInA/cNoYUyuD9b2BVwAB4oAnjDFbnB0015SqBd2nwYkt1lPjZe/C2tFw+1CrM4aLfL0WUtSPUb3q06dJDG/O28ET0//g9krBvNGhJlVLBdgdTymVe7yAcKAlEAqsFJHaxphzACJSGpgKPGKMSXHs8ypwEqtYHod1D387/YFFZBAwCCAkJITYWPv7YcTHxzs9h1/kx/hF7yau41ckXk7OUn+TnMiRHZrD9XK4QgbNkX1ZGZViEjAKmHKd9YeAFsaYsyLSHusm29g58WxUui70/BqO/WEVyEvfsr5ma/YM3DYAfAranRCAJhWD+enpO/h63Z98vGgv9362ir5NyvPc3VUo4u9tdzyl1K05BpRN8znUsSytKOB3Y0wicEhE9mIVyutFpDDwM/BvY8za1B2MMSccb6+IyFfAixmd3BgzDuueTkREhAkKCnLCJd2a2NhYnJrj+GbY8AXU7UVA/Qfty5FNmsP1crhCBs2RfZk2pTDGrASuW+obY9aktm0D1mLduN1HSH3oPQsGLIXSEbD4dRhZFyJHWz2YXYCXpwd9m4ax/MWW9GpUjimRh7nrk+V8ve5PklO0eYVSedh6IFxEKoiID9ADmJdumx+wnhYjIsWwmlYcdGw/B5hijJmddgfHU2RERIAHgO05eREuK+mq1YSiYDFo9392p1FKuQBnj2PcH1hwvZV5+ms5/4pw33i8TmzEb+1wvBf+i5RVw4lv+ARXavUCr1trouesrxqea16G+6oV4cMlh3j1+21MXn2Ql1uHERFaOFdz3ApXyKA5NIcrMMYkichTwELAE5hojNkhIm8DG4wx8xzr2orITiAZa7SJGBHpAzQHgkXkUcchH3WMQDFdRIpjNYHbDAzO3StzEb8Nh1PboccM8Au0O41SygVIVjpsiUgY8FNGbYzTbHMXMAa4wxgTk9kxIyIizObN9o8QlO1H/IdXWx30Dq+CgDJw5/NQ/+FsF8jO/qrBGMNPW0/wf/N3ceJ8Ap3rhTCsfTVKFvbN1RzZ4QoZNIfmyIyIbDTGNLQ7R27J8/fs9E7tgLEtoEYna2Qiu3LcIs3hejlcIYPm+Kes3rOdMiqFiNQBxgOdslIUu4WwZvDoT/DIjxBYHua/CJ/Vhw0Tra/nbCYidKhbhqUvtODpVpX5edsJ7vp4OZ8vP8CVJB3eTSmVjyUnwQ9Pgm8RaP+h3WmUUi7klgtjESkHfA/0NcbsvfVIeUyF5tBvAfT9AQqXgZ+eg/81gD+mQHKi3enw9/HihbZVWfJcC+6oXIwPftnNPcNXsnTXKR3eTSmVP635DE5shvs+hoLBdqdRSrmQTAtjEfkaiASqikiUiPQXkcEiktom7XUgGBjjmFp0Qw7mdU0iUOku6L8Ien8HhYrDvKdhVEPYNN16OmGzcsH+jHu4IVMea4Snh9B/8gb6TVrPgeiLdkdTSqncE73HGmmoekeo2dnuNEopF5Np5ztjTM9M1g8ABjgtUV4mYs2eV7k17FsEy96DuU/Cqo+hxTCo3QU8PG2N2LxKcX55tjmT1xxm5JJ9tBuxkseaVeCpVpUJ8NXh3ZRSbiwl2RqFwscf7vvE7jRKKRekM9/lBBGocg8MWmH1dvYuCHMGwejGsG22dXO2kbenBwPurMivL7akc70Qxq06SKtPVvDdxihStHmFUspd/f4FRK232hUXKmF3GqWUC9LCOCeJQLX74PGV0G0qeHrDd/1hTFPY/j2kpGR+jBxUPKAAH3apyw9PNiOkqB8vfLuFPpO3sWJvtLY/Vkq5l5gDsPQdqNIeane1O41SykVpYZwbPDygRkcYvBq6fGUtm90PvmgGO+fZXiDXLVuU75+4neHd63LhShKPTFxHzy/XsunPs5nvrJRSri4lxer34ekD939qPbRQSqkMaGGcmzw8oNaD8GQkPDTBGrViVl8Y2xyfXd9BwgUbowmd64UyZ0AEb3Wsyf7TF+k8Zg2PT93AvlNxtuVSSqlbtmECHFltzW5XuIzdaZRSLkwLYzt4eFod8Yb8Dp3HQeJlCi1+ET4Oh1kPw64fITHBlmjenh48cnsYK166ixfaVGH1/hjuGbGSl77dwrFzrjEFtlJKZdnZw7D4DajUGiJ6251GKeXinD0ltLoZHp5QtzvU6caFnUspfGSh1fZ451woUASqd7AK6ArNc300i4IFvHi6dTi9m5Tn8+X7mRx5hLmbj9O3aXmG3FWZoII+uZpHKaVumjEwb6jVdKLDSG1CoZTKlBbGrkCEpNL1oebdcM9/4dAKa/SKnXNh8zQoWMJqglGrC4Q2zNWbe1BBH/59Xw36NavAiCV7+Wr1Ib5Zf5SBd1ZkwJ0VKFhAf4SUUi7qj8nW/fS+T6FoWbvTKKXyAG1K4Wo8vaxxkDt/Di/tg25ToFwT2PAVTLgbPouApW/D6V25GqtMUT8+7FKXRc81547KxRi+ZC/NP1zGpNWHdIpppZTrOR8FC1+DsDuhQT+70yil8gh93OfKvP2gRifrlXAedv8M276F34bDqk+gRE2rqUWthyCwfK5EqlwigC/6NmDTn2f58Jc9vPnjTsb/dojn21ShU0QInh76VaVSymbGwI/PgkmGjv+zOj4rpVQW6N0ir/AtAhG9oO8ceGEPtP8IChSCpW/ByDowoS2s+xIuRudKnHrlApkxsDFT+zeiqL83z8/awr0jV7Fk5ykdA1kpZa8tX8P+xdD6DQiqYHcapVQeooVxXlSoBDQeBP0XwTNbrZv/lYsw/0X4pCpMfRA2f53jw7+JCHeGF2fekDsY1aseV5NTGDBlA12/iGTdodgcPbdSSmUo7iT8MgzKNoFGg+xOo5TKY7QpRV4XWB7ufN56ndoJ22dbzS1+GAxevtbU1LW6QHhb8PbNkQgeHsL9dcpwT81SfLshipFL99JtbCR3VS3Oy+2qUb104Rw5r1JK/Y0x8NPzkHQFOo3WJhRKqZumhbE7KVkDSr4Orf4DURusAnlH6vBvhf8a/i2sudXJz8m8PT3o1bgcneuFMDnyMGOW7efez1bRqW4Znm9TlXLB/k4/p1JKXbP9O9jzM7R5B4pVtjuNUioP0sLYHYlA2dus1z3/B4dXWsO/7foRNk+HgsWh5oNWkRx6m9OHf/Pz8WRwi0r0vK0cY1ceYOLqQ/y09QS9GpfjqVaVKRGQ7sl1chIkXoKrl+HqJTzOn4XAQB1zVCmVdRejYf5LENIAmg6xO41SKo/SwtjdeXpBpVbW675PYd8iq7nFxkmwbiwULe8Y2aKL9cT5epKu/lW8JloFrPXn5TTL066/SJGrl3k58TJDq8Xx58lozm08T/QfV/D0SyHQOxGPRMe2yVf/dqqiAEXKWqNx1HwQQuprkayUurEFL8HVi9BpTK5PiKSUch9aGOcn3r5Qo6P1SriQZvi3Edbwb8WrEeBdCFKupit6L0FK0s2dy8sXvP3BpxC+Pv5UCfAnvnAQB86lEHkBkjz9qBxagmplS+HlW8ixrT94F+TS+RgKRq2A38dC5CgoUg5qPgA1O0OZelokK6X+buc82DHHakZWoprdaZRSeZgWxvmVb2GI6Gm9LkbDzh9gz3y4Eg+Fiv2tUMXHH3wK/vX+2p+py/3/uTyDJzZ+QC2AY+f5aOEeVuyNpnS0L8/eHc5D9UPx8rQ6ylyJjaVg8ych/pyVacccWDsG1nxmPeFOLZJLR2iRrFR+dzkWfn4eStWBZs/YnUYplcdpYaygUHFoNBAaDSQuNpagoKAcPV2tkCJMfqwRkQdi+OCX3bzy3TbGrjzIS22r0q5Wqb829Ctqjd0c0Qviz1pPuHf8AJGjYfVICAyzCuSana3/KWqRrNyUiLQDRgKewHhjzPsZbNMNeBMwwBZjTC/H8keA1xybvWuMmexY3gCYhPU763zgGZMXByH/ZZh1f+g7Bzy97U6jlMrjtDBWtmlaKZg5T97Oop2n+GjhHp6Y/gd1Q4sw+PYytAsMRNIWun6BUK+P9boc6yiS58Dqz6yZAIMqWgVyjQegVG0tkpXbEBFPYDTQBogC1ovIPGPMzjTbhAOvAs2MMWdFpIRjeRDwBtAQq2De6Nj3LPA5MBD4HaswbgcsyL0rc4I9v8DWb6DFK9a/e6WUukWZDvIoIhNF5LSIbL/OehGRz0Rkv4hsFZH6zo+p3JWIcE/NUix8tjkfdalDdNwVnpi1i/YjVzE18jBxCYn/3Mk/COr3hb7fw4v/396dx1dR3f8ff32ykYUQyEpW1gQCouxBEVRAiqKobRW3/mp/bdXW3datdlFrW9tatbW21brUVmVRKiJuCOKuAWQRSNgEmoQlQSKLbLKc7x9zgUsACeHmzg15Px+PeXDvzNyZ9w3JySfnnjmzFM4NDLF4/yF4dBA83Aem/RrWLvDmNRVp2voDy5xzy51zXwFjgfPq7PND4JFAwYtzriaw/hvAm8652sC2N4ERZpYNtHLOfRzoJf43cH443kzIbNsAk2+EzO4w6Kd+pxGR40R9eoz/BfwVr+E8lLOAwsBSgtcLURKKcNJ8REcZF/bN59yTcnjm/SVMXPA5v3hpIb97bRGjTsrhspJ29MhLOfiFSWnQ57vesuVzb0q6sonw/gPw3v2QVrh/uEVmsXqSpSnKBSqDnldxcBtbBGBmH+ANt7jLOff6YV6bG1iqDrH+AGZ2JXAlQG5uLrW1/t/Rctu2bdTW1pI09Tbivqxh08hH2b3pS99y+E05Ii9HJGRQjoY7YmHsnHvXzNp/zS7nAf8O9Dp8bGatzSzbObcmRBmlGYmPjeabJ2XxgzOK+bRqA89+XMFLc1czdmYlJ+alcFlJAeeelENi3CG+dZPSoe/3vOXLdbDoZW+4xXv3w7t/gPQu+y/cyywO/5sTaTwxeJ0TpwN5wLtmdsxjC5xzjwGPAfTs2dM19vUH9VFbW0tq7RwoGw+n3kRK19P8yxEpXw/liKgckZBBORouFGOMD9cjcVBhHMm9D35TjoMz5CXCbUPy+PHAtry68HMmzF3LbRPm8+uXyzi7ezrf6plFYUbSYY4SDR3Ph47nY1vXEbfsDeKWvkLMO3/A3vk9u1IL+apwJF8VjmRP6qHvkBUJXwvliNwcYbQKyA96nhdYF6wKKHXO7QRWmNkSvEJ5FV6xHPzatwPr845wzMj01Zfw8g2QXgSn3e53GhE5zoT14ruI7X1QjojKUTdDKvCj7EyuHlrMJ//7gudKK5g4fw3j51TTp10bLisp4Owe2cTHHmZS/9RUbqY+zgAAG7dJREFUyOsCp18Pm6uhfBIxCycSU/pnEksfgsxu+y/cyyg6bA6/KEdk5gijmUChmXXAK14vBi6ts89E4BLgKTNLxxtasRz4DPitmbUJ7DccuMM5V2tmm8xsAN7Fd/8PeLjx38qxS/zgPthYBd+f4s3NLiISQqEojOvTmyFyzMyMvu1T6ds+lV+c040Js6t4rrSCm8fP457JZXyrdx6XlhTQKaPl4Q+SnLVvajo2r91/Y4Dpv4Xpv/Eu5Nk7JjmqWRVfEqGcc7vM7FrgDbzxw0865xaa2T3ALOfcpMC24WZWBuwGbnHOrQcws1/jFdcA9zjn9na3/5j907W9RlOYkWLFe8TPfxZOvhby+/udRkSOQ6EojCcB15rZWLwLQjZqfLE0tjZJcfxgUEe+f2oHPlq+nmdLK3j6w5U88f4KTu6YxqUlBXyje1viYr5m4pXktlBypbdsWu0VyWUTYfq9MP1eWqUWQcdBUDAA8kugdYEu3hNfOOdexZtSLXjdL4MeO+DmwFL3tU8CTx5i/SwC99xpEnZug0nXsTulHdFn3Ol3GhE5Th2xMDazMXhj1NLNrApvTsxYAOfcP/Aa67OBZcBW4HuNFVakLjPjlE7pnNIpnXWbd/D8J5U8V1rBdWPmkN4yjgv75nNJvwIK0hK//kCtcmDA1d6ycRWUT8ItnAyfjodZT3j7JGd7vVT5A6CgxLupiG4oIBIeb98HX6xgywXP0SruCD/PIiINVJ9ZKS45wnYHXBOyRCINlJHcgh+f3pmrB3fi3aXreLa0gkff+Yx/vPMZgwozuKykgKFdM/fdevqwUnJhwI/YXDSa1NYpUL0QKku9paIUyl7y9otJgNw+XpGcPwDy+3k3IhGR0FozDz58GHp9h135J/udRkSOY7rznRx3oqKM07tkcnqXTNZs3Ma4mZWMnVHJVf/5hLat4hndL5+L++eTnZJQj4NFQ/aJ3tL/h966Tav3F8mVH3s3FnG7vW0ZXb1hF3uHX6R21PALkWOxexdMug4S02D4r2GbbtojIo1HhbEc17JTErhxWBHXntGZtxbV8NyMCv7y1lIefmspQ7pmcdmAAgYXZhAddRTFa6uc/RfoAXy1BVbN9orkilJvnPLsp71tSRlegbx3yekJMS1C/0ZFjlcf/83rMb7wae8TmW3Naqo+EQkzFcbSLMRERzG8e1uGd29LZe1WxsyoYPysSqaWV5PXJoFL+hdwYd88MpMbMP1TXBJ0GOQtAHv2wOeLoeLjQM/yx7BosrctugXk9AoMvwgsSemhe6Mix5PaFd6MMV1GQre6d8EWEQk9FcbS7OSnJnLriK7cOKyIN8uqebb0f/zxjcU8+OYSvtG9LZeWFHByx7SGnyAqyruzXmaxdxc+gC9r9hfJlaXw0d/ggz9729I6B8Yo9/eGYKQXafiFiHMw+UaIioGR9+tnQkTCQoWxNFtxMVGMPDGbkSdms3zdl4yZUcHzn1Txyvw1dEhP4rwT0rn81CTSW4Zg6EPLTCg+11sAdm6H1XP2D79Y/CrMfcbbltAm0JvszYBhMZkQjzfe2aKClqDnUUe4oFCkqZk3Bpa/DSP/5A1fEhEJAxXGIkDHjJbcObIbPxnehdcWrOG50goeevt/PPJeBcOKsxjdL59BRzsW+evExkO7k70FvN6x9csCPcofQ+UMWPI6APWe5+LriuaGbKuzPZlYSE6HhNYQH1gS6vwbn7L/cUxcaL5W0vx8WQOv3+F9ktLn//udRkSaERXGIkHiY6O5oFceF/TKY9aSKl5fson/zlnFawvWkpMSz4V987mwbx55bUI8j6oZpBd6S+/veOu2rIeqGWxZXU5SQgK4Pd6yZ/f+x855M2IccludpaHb9m7fthHWfwbbN8D2jbBz69e/p9jEg4vlQxXQh1oXm6CPzpuz12/3vr9G/UWfhohIWKkwFjmMjumJ/Lwoj1tGdGFqWQ3jZlXyl7eW8pe3lnJq53Qu7lfAsG6ZtIiJbpwASWnQ5Sx2ZJSQlOr/7ak319aSGpxj1w6vQN62YX+xvPfxvnV7H2+EjVXenNDbN8COTV9/sui4wxTQKSTu3A0tAsNb3N6pu9yBj+tu2/f867btPQ6H33bAc2kUi1+HBRPgjDsho4vfaUSkmVFhLHIELWKi941FrvpiK8/PquL5WZVc89xsUpPiuKBXLqP75VOUlex31PCKaeGNnW6ZefSv3b3LK463feEVzcHF9N5COnjdlnXeUJPtG4jbvSswvGPvwQIPzOo8rrst8Pzrth3xOHXOKaG1YzO8cjNkFMPAG/1OIyLNkApjkaOQ1yaRm84s4vqhhby/7HPGzazg3x+t5In3V9C7oDUX9ytg5InZJLXQj9bXio6BxFRvOUob6vZc++UmFcchN+0e7wY6339aY9RFxBf67S3SANFRxmlFGZxWlMHnX+7gxdmrGDerklsnfMrdLy/k3JNyGN0vn575rTGNlRU5sopSmPFPKLnKu726iIgPVBiLHKP0li344eCO/GBQB2ZXfMHYGZW8NHc1Y2dW0iUrmdH98rmgVy5tktQDJnJIu3Z4t31OyYMhP/c7jYg0YyqMRULEzOjTLpU+7VL55bndmPzpGsbOrOSeyWXc99oihnfP4uJ+BZzSKY2oUE37JnI8eP9B726Rl70ALZrZWH0RiSgqjEUaQXJ8LJf0L+CS/gWUr9nEuJmVTJy7ismfriGvTQIXBaZ9y05J8DuqiL9qFsG790OPC6HwTL/TiEgzp8JYpJEVZ7firlHduf2srkwpq2bczAoeeHMJD01dwmlFGYzuV8DQ4kxiozVfqzQze/Z4QyhaJMOI+/xOIyKiwlgkXOJjoxl1Ug6jTsqhYv1Wnv+kkvGzKrn6mU9IbxnHt3rncVG/fDpltPQ7qkh4zHoCqmbABY9CUrrfaUREVBiL+KEgLZGfDO/CDUMLeXfpOsbOqOSJ91fw6LvL6d8+lYv65TOyRzYJcY108xARv22sgql3QachcOJov9OIiAAqjEV8FRMdxZCuWQzpmkXN5u38d/Yqxs2s5KfPz+PuSQsZ1TOHs4pSGNimjaZ9k+OHc/DKT7zbjZ/zoG7/LSIRo16FsZmNAP4MRAOPO+fuq7O9AHgaaB3Y53bn3KshzipyXMtMjufq0zpx1eCOzFhRy7hZlUyYXcWzpRV0bbuSi/pq2jc5Tix8EZa8DsN/A23a+51GRGSfI17tY2bRwCPAWUA34BIz61Znt58D451zvYCLgb+FOqhIc2FmlHRM44GLelL6s2H87MwOtIiJ4p7JZZT8dhrXPDubd5asY/ce53dUCQMzG2Fmi81smZndfojtV5jZOjObG1h+EFh/RtC6uWa23czOD2z7l5mtCNrWM2xvaGstvHYr5PSCkqvDdloRkfqoT49xf2CZc245gJmNBc4DyoL2cUCrwOMUYHUoQ4o0VykJsXy7V1uuHNqNRWs3MX5mFS/OqeKV+WvITonn233yuLBPPgVpiX5HlUYQ1DFxJlAFzDSzSc65sjq7jnPOXRu8wjk3HegZOE4qsAyYErTLLc65Fxot/OFM+YVXHH/nRe/W4CIiEaQ+rVIuUBn0vAooqbPPXcAUM7sOSAKGhSSdiOzTtW0rfnluN247qwvTymsYN7OSv05fxsNvLeOUTmlc1DefESe0JT5WF+wdR+rTMVEf3wZec85tDXG+o/PZdJj7DJx6M7Tt4WsUEZFDCdWf65cA/3LO/cnMTgb+Y2YnOOf2BO9kZlcCVwLk5uZSW1sbotM33LZt25QjwnJEQoZIzzEgtwUDcjuzdlM+kxes46X5Ndw4bi4tJ0ZzVnE65/XIpLhtUkgv2Ivkr8dxrD4dEwDfMrPBwBLgJudcZZ3tFwMP1Fn3GzP7JTAN77qQHXUPGtI2e+c2Ul66Hlq3Z+OJP4QGHitS/v+VQzkiOYNyNFx9CuNVQH7Q87zAumDfB0YAOOc+MrN4IB2oCd7JOfcY8BhAz549XWpqagNjh05tbS3KEVk5IiFDU8mRmgrd2mfz07MdH69Yz/Ozqpg0fw3Pz62ma9vkkF6w1xS+Hs3Uy8AY59wOM7sK70LoIXs3mlk20AN4I+g1dwBrgTi8Nvk24J66Bw5pmz3lF7CpAq54hdTMnAYfJlL+/5VDOSI5g3I0XH1utTUTKDSzDmYWh9fzMKnOPhXAUAAzKwbigXWhDCoihxcVZZzSKZ0HR/dkxp3DuPf8E4jTBXvHgyN2TDjn1gf19j4O9KlzjIuAF51zO4Nes8Z5dgBP4Q3ZaDyr58JHf4Xe34X2pzbqqUREjsURe4ydc7vM7Fq83oZo4Enn3EIzuweY5ZybBPwE+KeZ3YR3Id4Vzjn9BhbxQUpCLJcPaMflA9pRvmYT42dVMnHOKl2w1zTt65jAK4gvBi4N3sHMsp1zawJPRwHldY5xCV4P8UGvMW+szfnAgsYID8DunTDpWkjKgDMP6pQWEYko9RpjHJiT+NU6634Z9LgMGBjaaCJyrIqzW/Grc7tz+1ldmVpWw/hZ+y/YO7ljGqP76YK9SFbPjonrzWwUsAuoBa7Y+3oza4/X4/xOnUM/a2YZgAFzgcabN+2jR2DtfLjoP5DQutFOIyISCporR6QZaBETzcgTsxl5YjarN2xjwidVjP+kkhvHzSX5pRjO65nDRX3z6ZGbojvsRZh6dEzcQZ0e4aBtK/Eu4Ku7fsjBezeC9Z/B27+DrudAt1FhOaWIyLFQYSzSzOS0TuC6oYVcc0bnfRfsPT+rimc+rth3wd75vXJJ1R325Fg4By/fANFxcPb9fqcREakXFcYizdTeC/ZO6ZTOXaO68/K81YyfVck9k8u477VFnNktiwv75jGoMIPoKPUiy1Ga8wysfA/OeQhaZfudRkSkXlQYi0i9Ltg7o0NLmtCMO+KnzdUw5U5oN9CbiUJEpIlQYSwiBzj8BXvQMX0pQ4szGVacRZ92bYiJrs+Mj9LsvHYr7NwO5/4ZovQ9IiJNhwpjETmk4Av21mzcxsSZK/jwf5v514cr+ed7K2idGMuQLpkMLc5icFE6yfGxfkeWSLDoFSibCEN+DumFfqcRETkqKoxF5IiyUxIY3bstPxrWjc3bd/Le0s+ZWlbNW4tr+O+cVcRGGwM6pjGsOIuhxZnktdEcyc3S9k3wyk8hszuccoPfaUREjpoKYxE5KsnxsZzdI5uze2Sza/ceZldsYGp5NVPLqvnVpIX8atJCirNbMSww5KJHbgpRuniveZh2N2xeA6OfgRjNaiIiTY8KYxFpsJjoKPp3SKV/h1R+dnYxn637kmnl1Uwtr+GRwI1EMpNbMLQ4i2HFmQzsnK6biRyvKj6GmY/DgB9DXt27UouINA0qjEUkZDpltKRTRkuuHNyJL7Z8xfTFNUwrr+HleasZM6OC+NgoBhVmMKw4kyFds8hIbuF3ZAmFXTtg0nWQUgBn3Ol3GhGRBlNhLCKNok1SHN/sncc3e+exY9duSpfXMrW8mmnlNbxZVo3ZfHrmt2ZYcRbDirMoymqpu+41Ve/9CT5fApdNgBYt/U4jItJgKoxFpNG1iIlmcFEGg4syuHuUo3zN5kCRXM0f31jMH99YTH5qwr4iuX+HVGI1FVzTUF0G7z0AJ46GwmF+pxEROSYqjEUkrMyMbjmt6JbTiuuHFlK9aTvTymuYVl7Nc6UVPPXBSpLjYzi9SybDijM5vSiTlERNBReR9uz2hlDEt4Jv/M7vNCIix0yFsYj4KqtVPJeWFHBpSQFbv9rF+0s/9wrlRdW8PG810VFG//apDC3O5MxuWSRrtEXkmPk4rJoF3/wnJKX5nUZE5JipMBaRiJEYF8Pw7m0Z3r0te/Y45lXtnQquhntfKefeV8rJTWnBgE4ZlARmw2iXlqixyX7YUAlT74bOw6DHhX6nEREJCRXGIhKRoqKMXgVt6FXQhlu+0ZXK2q1MK6/m3cVrmb64hgmzqwDITG5B/w6pgUI5jcLMlpo3ubE5B6/c7D0+50HQHyYicpxQYSwiTUJ+aiJXDOzAqOIU2rRpw2frvqR0RS0zVtRSuryWyZ+uAaB1Yiz92qfu61Hult2KGF3IF1oLJsDSKTDiPmhd4HcaEZGQUWEsIk2OmdE5M5nOmclcVtIO5xxVX2wLFMrrmbGiljfLqgFIioumT1ChfGJeCi1idJORBtuyHl67FXL7QP8r/U4jIhJSKoxFpMkzM/JTE8lPTeTbffIAqN60nRmBHuUZK2r54xuLAWgRE0XP/Nb7hl70bteaxDg1hfU25U7YvhFGPQxR+gNDRI4v9fptYGYjgD8D0cDjzrn7DrHPRcBdgAPmOecuDWFOEZGjktUqnnNPyuHck3IA+GLLV8xcGSiUV9by1+nL2PPWMmKijBNyU/b1KPdtn0pKgqaHO6Rl02DeGBj0U8jq7ncaEZGQO2JhbGbRwCPAmUAVMNPMJjnnyoL2KQTuAAY6574ws8zGCiwi0hBtkuL2zXgBsHn7TmZXbNg39OKpD1by6LvLMYOubVtRErigr1+HVNJb6tbVuD0w+UZI6wyDb/E7jYhIo6hPj3F/YJlzbjmAmY0FzgPKgvb5IfCIc+4LAOdcTaiDioiEUnJ8LKcVZXBaUQYA23fuZm7lhn1DL8bNrORfH64EoFNGEv07pO3rVc5pneBjcn9EbamGDdvhilchNt7vOCIijaI+hXEuUBn0vAooqbNPEYCZfYA33OIu59zrdQ9kZlcCVwLk5uZSW1vbkMwhtW3bNuWIsByRkEE5mmeOotZGUa80Lu+Vxs7de1hUvYXZlZuYXbWZl+etYsyMCgByUsLbg3yk4WxmdgXwR2BVYNVfnXOPB7btBuYH1lc450YF1ncAxgJpwCfAd5xzXx0uQ9TW9dDnWmg/MGTvS0Qk0oTqipMYoBA4HcgD3jWzHs65DcE7OeceAx4D6Nmzp0tNTQ3R6RuutrYW5YisHJGQQTmUAyArI53TTvAe797jWLx2MzNWrKd0RS0fhSVB/YazBYxzzl17iENsc871PMT63wMPOufGmtk/gO8Dfz9cDheXBGfe3bA3ISLSRNRncs9VQH7Q8zz290rsVQVMcs7tdM6tAJbgFcoiIseF6CijW04rrhjYgb9f3iecp943nC3Qo7t3OFuDmXerwCHAC4FVTwPnf91rdrfuAPEpx3JaEZGIV58e45lAYeBjt1XAxUDdGScmApcAT5lZOt7QiuWhDCoi0kzVZzgbwLfMbDBex8RNzrm9r4k3s1nALuA+59xEvOETG5xzu4KOmVv3gBr+phzK0TQzKEfDHbEwds7tMrNrgTfwxrc96ZxbaGb3ALOcc5MC24abWRmwG7jFObe+MYOLiMg+LwNjnHM7zOwqvB7gIYFt7Zxzq8ysI/CWmc0HNtbnoBr+phzK0TQzKEfD1WuMsXPuVeDVOut+GfTYATcHFhERCZ0jDmer0xHxOPCHoG2rAv8uN7O3gV7ABKC1mcUEeo0PNURORKTZqc8YYxER8c++4WxmFoc3nG1S8A5mlh30dBRQHljfxsxaBB6nAwOBskBnxnTg24HXfBd4qVHfhYhIE6D7oIqIRLB6Dme73sxG4Y0jrgWuCLy8GHjUzPbgdYTcFzSbxW3AWDO7F5gDPBG2NyUiEqFUGIuIRLh6DGe7A+/uo3Vf9yHQ4zDHXI4344WIiARoKIWIiIiICCqMRUREREQAMO8aDB9ObLYZWOzLyQ+UDnzudwiUI9IygHLUpRwH6uKcS/Y7RLiozT6IchxIOSIrAyhHXfVqs/0cY7zYOdfXx/MDYGazlCOyckRCBuVQjvrk8DtDmKnNVg7laCIZlOPQOeqzn4ZSiIiIiIigwlhEREREBPC3MH7Mx3MHU44DRUKOSMgAylGXchwoUnKES6S8X+U4kHIcKBJyREIGUI666pXDt4vvREREREQiiYZSiIiIiIigwlhEREREBPCpMDazEWa22MyWmdn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       "Find user logs for this run at: <code>/content/wandb/run-20210922_062021-2rmci04e/logs/debug.log</code>"
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       "Find internal logs for this run at: <code>/content/wandb/run-20210922_062021-2rmci04e/logs/debug-internal.log</code>"
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       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>0.71654</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.58872</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.59588</td></tr><tr><td>valid_loss</td><td>0.89751</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr></table>"
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       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>▁▃▅▅▆▇████</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▇▆▆▄▄▄▄▃▃▄▃▄▂▃▃▃▃▃▃▃▂▃▂▂▂▂▂▂▂▁▂▁▂▂▂▁▁▁▂</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>█▇▇▆▆▅▅▅▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁</td></tr><tr><td>valid_loss</td><td>█▅▄▃▃▂▁▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
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       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">cosmic-feather-40</strong>: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/2rmci04e\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/2rmci04e</a><br/>\n",
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       "\n",
       "                Tracking run with wandb version 0.12.2<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">dainty-voice-41</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/2uik45rg\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/2uik45rg</a><br/>\n",
       "                Run data is saved locally in <code>/content/wandb/run-20210922_062046-2uik45rg</code><br/><br/>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.983263</td>\n",
       "      <td>1.496564</td>\n",
       "      <td>0.548662</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.531047</td>\n",
       "      <td>1.495873</td>\n",
       "      <td>0.545418</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.287402</td>\n",
       "      <td>1.133325</td>\n",
       "      <td>0.651257</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.118862</td>\n",
       "      <td>1.234268</td>\n",
       "      <td>0.632603</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.996022</td>\n",
       "      <td>0.962171</td>\n",
       "      <td>0.699513</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.904068</td>\n",
       "      <td>0.976103</td>\n",
       "      <td>0.686131</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.807420</td>\n",
       "      <td>0.949470</td>\n",
       "      <td>0.701946</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.712814</td>\n",
       "      <td>0.915579</td>\n",
       "      <td>0.716139</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.632889</td>\n",
       "      <td>0.886929</td>\n",
       "      <td>0.718167</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.577995</td>\n",
       "      <td>0.878951</td>\n",
       "      <td>0.721006</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "output_type": "display_data"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
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       "<br/>Waiting for W&B process to finish, PID 2502<br/>Program ended successfully."
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    {
     "data": {
      "text/html": [
       "Find user logs for this run at: <code>/content/wandb/run-20210922_062046-2uik45rg/logs/debug.log</code>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "Find internal logs for this run at: <code>/content/wandb/run-20210922_062046-2uik45rg/logs/debug-internal.log</code>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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      "text/html": [
       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>0.72101</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.64854</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.57799</td></tr><tr><td>valid_loss</td><td>0.87895</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr></table>"
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       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>▁▁▅▄▇▇▇███</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▇▆▅▄▅▅▄▃▄▄▃▂▂▃▃▃▃▃▃▃▂▃▃▂▂▂▃▁▂▁▂▁▂▂▂▂▁▂▂</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>█▇▇▆▆▅▅▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁</td></tr><tr><td>valid_loss</td><td>██▄▅▂▂▂▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
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     "data": {
      "text/html": [
       "Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">dainty-voice-41</strong>: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/2uik45rg\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/2uik45rg</a><br/>\n",
       "                "
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       "\n",
       "                Tracking run with wandb version 0.12.2<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">glowing-pond-42</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/22fxmt6z\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/22fxmt6z</a><br/>\n",
       "                Run data is saved locally in <code>/content/wandb/run-20210922_062111-22fxmt6z</code><br/><br/>\n",
       "            "
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.046594</td>\n",
       "      <td>1.536608</td>\n",
       "      <td>0.533252</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.542286</td>\n",
       "      <td>1.280013</td>\n",
       "      <td>0.591241</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.277843</td>\n",
       "      <td>1.080615</td>\n",
       "      <td>0.650852</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.117956</td>\n",
       "      <td>1.014370</td>\n",
       "      <td>0.674371</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.996434</td>\n",
       "      <td>1.029199</td>\n",
       "      <td>0.665045</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.909742</td>\n",
       "      <td>1.091028</td>\n",
       "      <td>0.651663</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.812353</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>0.706002</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.712129</td>\n",
       "      <td>0.898865</td>\n",
       "      <td>0.715734</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.628552</td>\n",
       "      <td>0.891999</td>\n",
       "      <td>0.720195</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.571681</td>\n",
       "      <td>0.885267</td>\n",
       "      <td>0.722628</td>\n",
       "      <td>00:01</td>\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br/>Waiting for W&B process to finish, PID 2538<br/>Program ended successfully."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "311aa7b6865c4bf3ad7b4cbc7b8b56ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find user logs for this run at: <code>/content/wandb/run-20210922_062111-22fxmt6z/logs/debug.log</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find internal logs for this run at: <code>/content/wandb/run-20210922_062111-22fxmt6z/logs/debug-internal.log</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>0.72263</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.36249</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.57168</td></tr><tr><td>valid_loss</td><td>0.88527</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>▁▃▅▆▆▅▇███</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▇▆▆▆▅▄▃▃▄▃▄▃▃▄▂▂▃▃▂▂▂▁▂▂▂▂▂▁▂▂▂▁▂▂▂▁▂▂▁</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>██▇▆▆▅▅▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁</td></tr><tr><td>valid_loss</td><td>█▅▃▂▃▃▁▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
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       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">glowing-pond-42</strong>: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/22fxmt6z\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/22fxmt6z</a><br/>\n",
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       "\n",
       "                Tracking run with wandb version 0.12.2<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">vocal-glitter-43</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/12qqnlo7\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/12qqnlo7</a><br/>\n",
       "                Run data is saved locally in <code>/content/wandb/run-20210922_062136-12qqnlo7</code><br/><br/>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.029640</td>\n",
       "      <td>1.507968</td>\n",
       "      <td>0.541363</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.553599</td>\n",
       "      <td>1.197012</td>\n",
       "      <td>0.643147</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.285845</td>\n",
       "      <td>1.071525</td>\n",
       "      <td>0.645985</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.112243</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>0.658151</td>\n",
       "      <td>00:01</td>\n",
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       "      <td>0.974403</td>\n",
       "      <td>1.022519</td>\n",
       "      <td>0.687348</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.870751</td>\n",
       "      <td>0.996105</td>\n",
       "      <td>0.684104</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.758886</td>\n",
       "      <td>0.922510</td>\n",
       "      <td>0.705596</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.661384</td>\n",
       "      <td>0.914093</td>\n",
       "      <td>0.711679</td>\n",
       "      <td>00:01</td>\n",
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       "      <td>0.569740</td>\n",
       "      <td>0.909809</td>\n",
       "      <td>0.714923</td>\n",
       "      <td>00:01</td>\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O3I3c0L46FSPDCprdGFNKxcbG0rFjR5o2bUp4eDiVKp3qitWjRw/eeustGjVqRIMGDf7sQmFKriZNmpCWlkZ8fDxVqlRh8ODBXHXVVTRr1ow2bdrQsGHDs+5/5513MmTIEBo1akSjRo1o3bq1j5Ibc4FUYf9m2LEMtv8G25c5748dJPLkNt5dG+LqQa0uOVp6PUVvSMFGbSjpCtJi3BG4CVgpIss8y/4JVAdQ1bdwpivtBSTjDNc25FxC/KNHQ2b9vouXv1vP0/18/9WsMab4GT9+fK7LQ0NDmTZtWq7rNm/eDEBcXByrVq36c/lDDz1U6PmMb61ceerBv7i4OBYsWJDrdocPHwacEUpO/g6Eh4czceLEog9pzIVQhQNbThW/JwvhYwec9QHBUKkJNLkGqiZyMKwa5ao1OqNrgzm7goxKMR84a7u5ZzSKu883RK24stzYoQYfLdzCbR1rUrdiZP47GWOMMcaURKpwcOup4nf7b04xnL7fWR8QDJUaQ+O+ULUlVE2Eio0hKPTPQ2SlpkJU8enb6y/85p8Q93ary2dLUnh22lreu6Wt23GMMcYYY4qeKhxMOb0VePtvkO4ZCScgCCo2gkZXQZVEpxCu1OS0ItgUHr8pjGMjQrmjax2en7GOhRv30aF2bP47GWOMcZWqlprZSwt7aEJTCqk6D8CdbAE+WQgf3eusl0Cn5bdhL6cAruIpgoPt+Stf8ZvCGGBop1p8vHALz0xdwxd3dSQgoHTcbI0xpjgKCwtj3759xMbGlvjiWFXZt28fYWFWoJgCUnWGPfPuCrH9Nziyx1kvgVChIdTv4XSFONkS7Kfj+5YWflUYhwUH8rfLG/DQ/5bzzcod9GlR1e1Ixhhj8pCQkEBKSgp79uzx2Tmzs7MJCHBnptSwsDASEhJcObcpJg7tIOyXtyB1jVMIH97lLJcApwiue9mpPsGVmtpIEH7IrwpjgH4t43l//iaen7GWK5pUIjSo8Oa/NsYYU3iCg4P/nIbbV/xlwhtjzrDma5hyL+HHDkJcfajT7VSf4MpNIaSs2wlNAbjzz+6zCAwQRvZsyNbUdD5acPZB2Y0xpiAiIiIA2L59O/379891m65du7J48WJfxjLGlATHD8OUe+HTG6F8DQ7eOBPu/gX6vQUd7oDq7a0oLkb8rjAG6Fy/AhfXi+O12ckcPJrhdhxjTAlRtWpVJk+e7HYMY0xJsW0JvN0Zln4EnR6AobPIjq7jdipzAfyyMAYY2bMRh45l8MacZLejGGP8zCOPPMIbb7zx5+dRo0bx5JNPcumll9KqVSuaNWvGV199dcZ+mzdvpmnTpgCkp6czcOBAGjVqRL9+/UhPT/dZ/nMlIj1EZJ2IJIvII7msf0lElnleSSJywGvdLSKy3vO6xWt5axFZ6Tnmq1LSn54zpjBlZ8G8F+H9yyHzGNzyNXQfBUEhbiczF8jv+hif1LhqFNe0TGDsT5u5qUMNqsVYB3Vj/M60R2Dnyvy3OxeVm0HPZ8+6yYABA7j//vu5+25nXqFJkyYxY8YMRowYQVRUFHv37qVDhw706dMnz9ESRo8eTZkyZVizZg0rVqygVatWhXsdhUREAoE3gMuAFGCRiExR1d9PbqOqD3htfy/Q0vM+BngcaAMosMSz735gNDAM+AVn9tIeQO5TBhpjTjmwFb74K2z5CRpfDVe9DOHRbqcyhcRvW4wBHrqiPiLw4sx1bkcxxviRli1bsnv3brZv387y5cuJjo6mcuXK/POf/6R58+Z0796dbdu2sWvXrjyPMXfuXG688UYAmjdvTvPmzX0V/1y1A5JVdaOqngAmAn3Psv0gYILn/RXALFVN9RTDs4AeIlIFiFLVhZ6ZS8cBVxfdJRhTQqz6DEZ3hB3L4erRcN1YK4pLGL9tMQaoUi6coZ1q8eacDQztVJtmCeXcjmSM8ZZPy25Ruu6665g8eTI7d+5kwIABfPLJJ+zZs4clS5YQHBxMzZo1OXbsmGv5ClE8sNXrcwrQPrcNRaQGUAuYfZZ94z2vlFyWG2Nyc+wQTPsHLJ8ACW3hmncgprbbqUwR8OvCGOCOrnWYuGgrT09dw/hh7Uv8IPLGmIIZMGAAw4YNY+/evfz4449MmjSJihUrEhwczA8//MCWLWcf1aZz586MHz+ebt26sWrVKlasWOGj5EVqIDBZVbMK42AiMhwYDhAfH09qamphHPaCpKenWw7L4dMcQTuWUHbGAwSkbeNYu/tIb3cPEAR5nKck/1kU5xwF5feFcVRYMCO61WXU178zZ90eLmlY0e1Ixhg/0KRJE9LS0oiPj6dKlSoMHjyYq666imbNmtGmTRsaNmx41v3vvPNOhgwZQqNGjWjUqBGtW7f2UfJztg2o5vU5wbMsNwOBu3Ps2zXHvnM8yxNyLD/jmKr6DvAOQGJiovrD+MH+Mo6x5SgFObIyYe7zMPc5KJcAQ6YRXr0D+c1LVyL/LEpAjoLy+8IY4Ib2NRj782aembaGi+vFERTo112jjTE+snLlqQf/4uLiWLBgQa7bHT58GICaNWuyatUqAMLDw5k4cWLRh7xwi4B6IlILp3gdCNyQcyMRaQhEA95/CDOAp0XkZCfIy4GRqpoqIodEpAPOw3c3A68V4TUYU7ykboLPh0PKr9B8IPR6HsKi3E5lfKBYVJghQQE83KMhSbsOM3lJSv47GGNMCaGqmcA9OEXuGmCSqq4WkSdEpI/XpgOBiZ6H6U7umwr8P5ziehHwhGcZwF3Ae0AysAEbkcIYUIVlE+Cti2HPOrj2fbjmbSuKS5Fi0WIM0KNpZVpVL89/ZyXRJ7EqZUKKTXRjjLkgqjoVZ0g172WP5fg8Ko99xwBjclm+GGhaeCmNKebS98M3D8Lqz6FGR2fmuvLV3U5lfKxYtBgDiAiPXtmI3WnHeW/eJrfjGFOqeTVKlnil6VqNKbU2z4fRnWDNFLj0MWfCDiuKS6V8C2MRGSMiu0VkVR7ry4nI1yKyXERWi8iQwo/paF0jhh5NKvP2jxvYk3a8qE5jjDmLsLAw9u3bVyoKRlVl3759hIWFuR3FGFMUMk/Ad/+Gsb0hKBSGzoSL/wYBgW4nMy4pSH+EscDrOAPA5+Zu4HdVvUpEKgDrROQTz0D0he7hng35bs0uXv4uiaf6NSuKUxhjziIhIYGUlBT27Nnjyvmzs7MJCPDdl11hYWEkJCTkv6ExpnjZux4+ux12LINWN8MVz0BohNupjMvyLYxVda6I1DzbJkCkOAMMRwCpQGahpMtFrbiyDG5fnY9/+YMhHWtRt6L9EhvjS8HBwdSqVcu18xe3oX+MMX5GFZZ+CNNHOq3E138Ejfvkv58pFQrjCbbXgSnAdiASGKCq2bltWFiDxd/cugKTl6Tw5JQV/Peas49Vmh9/GXjacvhXBsthOYwxJdCRffD1CFj7DdTq4jxgF1XV7VTGjxRGYXwFsAzoBtQBZonIPFU9lHPDwhosPiYG7rqkLs/PWEfyQWhX6/xbj/yl9cly+FcGy2E5jDElzIbZ8MWdkJ4Klz8JHe4GH3bLMsVDYfxGDAE+V0cysAm4sGbcAritYy0qR4Xx1NQ1peIhIGOMMcach8zjMONR+KgfhJWD27+Hv9xrRbHJVWH8VvwBXAogIpWABsDGQjjuWYWHBPK3y+uzfOsBvlmxo6hPZ4wxxpjcZGc5/Xb90e418G43WPA6tB0Gw+dAleZupzJ+LN+uFCIyAegKxIlICvA4EAygqm/hzKo0VkRWAgI8rKp7iyyxl2taJfD+/E08N2MtlzepRGiQDa9ijDHG+Mwfv8DEQUQfT4PIKhAVD1FVnH67kVWdn1FVnXWRlSEw2De5VGHRezDzXxASAYM+hQY9fHNuU6wVZFSKQfms3w5cXmiJzkFggDCyVyNuGfMrHy/8g6Gd3HtS3hhjjClVNs2D8QMgsjLHGl5L+IlUSNsB25bAmm8gK+d8AwIRFXMUzZ5i2ruoDil7YbkO74av7ob1M6HuZXD1m855jSmAYj+vcpf6Fbi4XhyvzV5P/9YJlAv30b9GjTHGmNIq+TuYOBiia8LNX5GeEUK490OxqnA0FdK2w6EdcGibUzQf2uZ8Tt0IW+bDsYNnHjus3OmFs3fL88miukwMiJyxa/Cm2fD9w3A8DXo+D+2G5bqdMXkp9oUxwCM9G9L7tfm8+UMyI3s1cjuOMcYYU3Kt/Rb+dytUaAA3fQll4yDnEIoiUDbWeVU+y2RcJ47kKJy3O6+TRfSu1XB4F86UCV4CQ3O0NleFo/uIXPYJVGoKt34DFa0eMOeuRBTGTaqWo1/LeD74eTM3XVSDhOgybkcyxhhjSp7VXzizxVVpATd+BuHRF3a8kLIQV9d55SUrwymOTxbNh7Z7tURvh22LYc12yMogveVQwq98xpm4w5jzUCIKY4CHLm/Atyt28OLMJF4akOh2HGOMMaZkWT4RvrwTqrWHGyZBWJRvzhsYDOUSnFdeVCHzGOlp6YRbUWwuQIkZxK9q+XBu61SLL37bxqptufRZMsYYY8z5WTIWvrgDanZyWop9VRQXlAgEh7udwpQAJaYwBrizax2iywTztE36YYwxxhSOX96Gr++Dut2dluILHTXCGD9WogrjqLBgRlxaj5837GNO0h634xhjjDHF2/yXYdo/oGFvGPiJtcqaEq9EFcYAg9vXoGZsGZ6dupasbGs1NsYYY86ZKsz5D3z3ODS9Fq4baw+0mVKhxBXGIUEB/KNHQ9btSmPykq1uxzHGGGOKF1X4/t8w52locQNc867vZqwzxmUlrjAG6Nm0Mi2rl+e/s5I4eiLT7TjGGHPeRKSHiKwTkWQReSSPba4Xkd9FZLWIjPcsu0RElnm9jonI1Z51Y0Vkk9c6G8rHOFRh+kiY/xK0uQ36vgEBgW6nMsZnSmRhLCI82qsRuw4d5/15m9yOY4wx50VEAoE3gJ5AY2CQiDTOsU09YCTQUVWbAPcDqOoPqpqoqolAN+AoMNNr17+fXK+qy3xwOcbfZWfDNw/AL6Ohw11w5X8hoESWCcbkqcT+xrepGcMVTSrx1o8b2JOWc752Y4wpFtoByaq6UVVPABOBvjm2GQa8oar7AVR1dy7H6Q9MU9WjRZrWFF/ZWfDV3bDkA+j0IFzxtE2lbEqlElsYAzzcoyHHMrN55fskt6MYY8z5iAe8H5ZI8SzzVh+oLyI/ichCEemRy3EGAhNyLHtKRFaIyEsiYk9VlWZZGc5sdsvHwyWPwqWPWVFsSq0SM/NdbmpXiOCGdtUZ/+sfDOlYizoVItyOZIwxhS0IqAd0BRKAuSLSTFUPAIhIFaAZMMNrn5HATiAEeAd4GHgi54FFZDgwHCA+Pp7U1NSiu4oCSk9PtxyFmSPzOBHTRxCycSZHOz7CsWa3w/79vs9RSPwhhz9ksBznr0QXxgD3da/HF79t4z/T1vLOzW3cjmOMMediG1DN63OCZ5m3FOAXVc0ANolIEk6hvMiz/nrgC896AFR1h+ftcRH5AHgot5Or6js4hTOJiYkaExNzgZdz4VJTU7EchZQjIx0+HQ4bZ0HP5ynTfjhl3MhRiPwhhz9ksBznr0R3pQCIiwjlji61mfn7Ln7dVHz+xWKMMTjFbT0RqSUiIThdIqbk2OZLnNZiRCQOp2vFRq/1g8jRjcLTioyICHA1sKoowhs/duIIjL8ekr+Dq16F9sPdTmSMXyjxhTHA0E61qRQValNFG2OKFVXNBO7B6QaxBpikqqtF5AkR6ePZbAawT0R+B37AGW1iH4CI1MRpcf4xx6E/EZGVwEogDniyqK/F+JFjh+Dja2HzfOj3NrS+xe1ExviNfLtSiMgYoDewW1Wb5rFNV+BlIBjYq6pdCjPkhQoPCeRvlzXgH5+tYOrKnVzZvIrbkYwxpkBUdSowNceyx7zeK/Cg55Vz382c+bAeqtqt0IOa4iF9v1MU71gO/cdAk35uJzLGrxSkxXgskNtTzgCISHngTaCPZwzN6wonWuG6tnUCDStH8tyMtZzIzHY7jjHGGONbR/bCh1fBzpVw/UdWFBuTi3wLY1WdC5ytc+4NwOeq+odn+9zG0HRdYIDwSM+GbNl3lI8XbnE7jjHGGOM7abtg7JWwdz0MmgANe7mdyBi/VBijUoxYDpAAACAASURBVNQHgkVkDhAJvKKq43Lb0O2hf5rFBdK+Rjle+S6JS2uXJTIsyG+GEbEc/pXBclgOY0qMg9tgXB84tAMGT4ZaF7udyBi/VRiFcRDQGrgUCAcWiMhCVT1jVg1/GPrn//o046rX5zNheSqP9GzoN8OIWA7/ymA5LIcxJcL+zfBhH6dv8U1fQPX2bicyxq8VxqgUKcAMVT2iqnuBuUCLQjhukWgaX45+ifGM+WkT2w6kux3HGGOMKRp7k+GDXnDsINz8lRXFxhRAYRTGXwGdRCRIRMoA7XGGFfJbf7uiAQAvzljnchJjjDGmCOxeC2N7QeZxuPVbiG/ldiJjioV8C2MRmQAsABqISIqIDBWRO0TkDgBVXQNMB1YAvwLvqapfDxYfXz6c2zrW4otl21i764jbcYwxxpjCs2OFUxQjMGQqVM51pFVjTC7y7WOsqoMKsM3zwPOFkshH7uxah08X/cFTMzbwae0qRISW+NmxjTHGlHTblsBH/SAkEm6ZArF13E5kTLFSKma+y0258GCevbY5a3cd4baxizh6ItPtSMYYY8z5+2MhfNgXwqOdlmIrio05Z6W2MAa4okllnuxdj8WbU7n9w8Ucy8hyO5Ixxhhz7jb+6LQUR1aGIdMguobbiYwplkp1YQxwRaM4XriuBQs27mPYOCuOjTHGFDPrv4Px10N0TaelOKqq24mMKbZKfWEMcE2rBP5zTXPmrd/LXZ8stSmjjTHGFAvBG2bChIEQVx9u+QYiKrodyZhizbXCWLKOu3XqXF3fthpP9WvK7LW7uWf8UjKyrDg2xhjjp7IyYek4IqbdDVVawC1fQ9lYt1MZU+y5NhRD4P6NkLoJYmq5FeEMg9vXIDNLeXzKau6b+BuvDmxJUKA1qhtjjPETB7bC0nHw20eQtoPMqu0IvvlzCI10O5kxJYKLY5QpjB8At8+CsHLuxcjhlr/UJCMrmye/XUNQwHJeGpBIYIC4HcsYY0xplZ0F62fC4g8geRaoQt3ucOWLpMW1JcaKYmMKjWuFcVa5GpC6Af43BG6YBIH+M47w7RfXJiNL+c/0tQQFCi/0b0GAFcfGGGN86eA2p2V46Tg4tA0iKkGnB6HVzadGnUhNdTejMSWMa9WoBpeF3v8HU+6FGSOhl3/ND3Jn1zpkZGXz31lJBAcE8Mw1zaw4NsYYU7SysyD5e1jyASRNB82GOt2gx7PQoCcEBrud0JgSzd1m2lY3w94k+Pk154nadsNcjZPTiEvrkZGVzWuzkwkKFJ68uikiVhwbY4wpZId2wG8fw9IP4eBWKFsBOt4HrW7xq2dxjCnp3O+/0P3fsG8DTHsYYmpD3UvdTnSaBy+rT0aW8taPGwgODODxqxpbcWyMMebCZWfDxtlO3+F100CzoFYXuPz/QYMrISjE7YTGlDruF8YBgXDNuzCmB/zvVhg6Cyo2dDvVn0SEh3s0ICMrm/fnbyI4UPhnr0ZWHBtjfEZEegCvAIHAe6r6bC7bXA+MAhRYrqo3eJZnASs9m/2hqn08y2sBE4FYYAlwk6qeKOJLMQBpuzx9hz+EA39AmTj4yz1O67BN42yMq9wvjAFCI2DQBHi3G0wYALfP9qvxGEWEf13ZiIysbN6dt4ngwAD+fkUDK46NMUVORAKBN4DLgBRgkYhMUdXfvbapB4wEOqrqfhHxnuUhXVUTczn0f4CXVHWiiLwFDAVGF9mFlHbZ2bBpjqd1eCpkZ0LNi6H7KGjYG4JCXQ5ojAF/KYwByldziuOxV8KnN8LNX/rVjUJEGHVVEzKylDfnON0qHrisvtuxjDElXzsgWVU3AojIRKAv8LvXNsOAN1R1P4Cq7j7bAcX5V3034AbPog9xWputMC5sh/fAso9hyYewfxOEx0D7O6D1EIir63Y6Y0wO/lMYAyS0gavfhMm3wdf3O+/9qFU2IEB46uqmZGZl88r36wkOFO7pVs/tWMaYki0e2Or1OQVon2Ob+gAi8hNOd4tRqjrdsy5MRBYDmcCzqvolTveJA6qa6XXM+JwnFpHhwHCA+Ph4Uv1gaLD09HT/z6FKUMoCQleNJ2TDTCQ7g4yq7Tje7j5O1OlxqtGnEK6jWPx5lLIc/pDBcpw//yqMAZpeC3uTYc7TUKE+dHrA7USnCQgQnr22OZnZygszkwgODOCvXaxPmDHGVUFAPaArkADMFZFmqnoAqKGq20SkNjBbRFYCBwtyUFV9B3gHIDExUWNiYook/LlITU3Fb3Mc2QfLPoElY51x+sPKO6Mttb6V4AoNKIqB1vz6z6OU5vCHDJbj/OVbGIvIGKA3sFtVm55lu7bAAmCgqk6+oFRd/uEM4/bdKIitC42uuqDDFbbAAOH5/s3JyMrmmWlrCQ4M4LZONpyOMaZIbAOqeX1O8CzzlgL8oqoZwCYRScIplBep6jYAVd0oInOAlsBnQHkRCfK0Gud2TFMQqrDlJ6fv8JopkHUCqnVw/j/WuC8Eh7ud0BhzDgrSYjwWeB0Yl9cGnodD/gPMLJRUItD3ddi/GT4fDkOmQdXcnh1xT1BgAC8NSCQrW3nim98JDhRuuqim27GMMSXPIqCeZxSJbcBATvUNPulLYBDwgYjE4XSt2Cgi0cBRVT3uWd4ReE5VVUR+APrjjExxC/CVby6nZJD0/bBggtM6vDcJQss5/YbbDIGKjdyOZ4w5T/kWxqo6V0Rq5rPZvTgtEG0LIZMjOBwGjof3LoUJA2HYDxBVpdAOXxiCAwN4ZWBLMj5Zyv99tZqgwAAGtavudixjTAmiqpkicg8wA6f/8BhVXS0iTwCLVXWKZ93lIvI7kAX8XVX3ichfgLdFJBsIwOljfPKhvYeBiSLyJPAb8L6PL81/ZWXCkT1weBcc3u35efr78tuWOK3DCe2g75vQpB+ElHE7uTHmAl1wH2MRiQf6AZdQmIUxQGQlGDQRxlzhFMdDpvndjSckKIA3Brfkrx8t4Z9frCQoQLiuTbX8dzTGmAJS1anA1BzLHvN6r8CDnpf3Nj8DzfI45kacES9KB1VI3+9V3OZW8Hp+Ht2HMxx0DqHlIKIiRFTieNNBhF00HCrn2cPQGFMMFcbDdy8DD6tqdn7j+p7XE84hVQm+4mUivh5GxqdDONzrDZCAQojtKKynJZ+5sjYPHD/BPyav4Hj6UXo1qeBKjgvlDzn8IYPlsBymmDhxFI7szrvQTdvp/Dyy22nhzSkwFCIqOQVvdE2o1u7U54hKXu8rntZf+GhqKmHF6IEiY0zBFEZh3Abn6ziAOKCXiGR6hgQ6zXk/4RxzHRzfRcjMR4lZNhou/b9CiO0ozKclP7jtIm4bu4jHpiYTXS6KK5sXvOuHvzy16Q85/CGD5bAcxg/tXEnZ75+FE/tPFb7HD+WyoUDZCqeK2goNvQrdHAVvWDm/GhbUGOOuCy6MVfXP4RhEZCzwTW5F8QW76G7nAYd5L0BcfWgxoNBPcaHCQwJ5/9Y23DpmESMm/kZggNCjaWW3YxljTMnw0yuEbJwFCW2hcvM8WnYrQZlYCPS/0UiNMf6vIMO1TcAZGzNORFKAx8EZjlFV3yrSdKcHgStfhNSNMOUeiK4B1Tv47PQFVSYkiDFD2nLz+79w74SljB7cmu6NK7kdyxhjiresTFg/ixP1exM6YIzbaYwxJVS+nXVVdZCqVlHVYFVNUNX3VfWt3IpiVb31gscwPpvAYLh+HJSrBhMHO8O5+aGI0CDG3taORlWiuOuTpcxZd9bZWY0xxuQn5Vc4doATtS51O4kxpgQrvKfYfKVMDNwwCbIzYfxAOJZb/zL3RYUF89Ft7alXKYLhHy1h/vq9bkcyxpjiK2k6BASTUa2T20mMMSVY8SuMAeLqOi3H+9bD5Nucr9j8ULkywXw8tD2148py+7hFLNiwz+1IxhhTPCXNgJodITTS7STGmBKseBbGALW7OH2Ok2fBzEfdTpOn6LIhfHx7e6pFl2Hoh4tYtNmGmTLGmHOSugn2rIX6PdxOYowp4YpvYQzQ+la46B745S1Y9J7bafIUFxHKJ8PaUzkqjCEfLGLpH/vdjmSMMcXH+pnOz/pXuJvDGFPiFe/CGOCyJ5xWhKn/gA2z3U6Tp4qRYYwf1oHYiBBuGfMrK1IOuB3JGGOKh6TpzjCdMbXdTmKMKeGKf2EcEAjXvucM4D7pVtiT5HaiPFUuF8aEYR0oXyaYm97/lVXbDrodyRhj/NvxNNg831qLjTE+UfwLY3AexrhhIgSFwPjr4aj/9uOtWj6c8bd3oGxIIDe9/wtrd/rnqBrGGOMXNs5xpnK2/sXGGB8oGYUxQPnqMHACHNoOn94ImSfcTpSnajFlmDC8AyFBAQx+9xfW70pzO5IxxvinpOnOtM3V2rudxBhTCpScwhigWlu4+k3Y8hN88wCoup0oTzViyzJhWAcCAoQb3vuFjXuPuh3JGGP8S3Y2JM2Eut2dCZ6MMaaIlazCGKBZf+jyMCz7GH5+1e00Z1W7QgQThrVHVRk8bgXvzdtIVrb/FvPGGONT23+DI7utG4UxxmdKXmEM0HUkNLkGZj0Oa75xO81Z1a0YybcjLqZdjfI8+e0arnvrZzbsOex2LGOMcV/SdJAAp8XYGGN8oGQWxiJOl4r4VvD5MNix3O1EZ1UpKoyXr2nASwNasGHPEXq9Mo935m6w1mNjTOmWNN3pW1wmxu0kxphSomQWxgDB4c7DeOExMGEQpO10O9FZiQj9WiYw64HOdK5fgaenrqX/Wz+TvNsezDPGlEKHtsPOFdaNwhjjUyW3MAaIrOQM45Z+wCmOT/j/A24Vo8J456bWvDIwkU17j9Dr1fmMnrOBzKxst6MZY4zvJM1wflphbIzxoZJdGANUbuZMALL9N/jyTucpZz8nIvRNjGfWA13o1qAi/5m+lmtH/0ySDetmTKkjIj1EZJ2IJIvII3lsc72I/C4iq0VkvGdZoogs8CxbISIDvLYfKyKbRGSZ55Xoq+spsKQZUL4GVGjgdhJjTClS8gtjgIa9nKmjf/8S5jzjdpoCqxAZyugbW/HaoJb8kXqU3q/O540fkq312JhSQkQCgTeAnkBjYJCINM6xTT1gJNBRVZsA93tWHQVu9izrAbwsIuW9dv27qiZ6XsuK+lrOSUa6M7FH/R7OMyPGGOMjpaMwBvjLvdDyRpj7HKyY5HaaAhMRrmpRlVkPdqF744o8P2Md/d782WbMM6Z0aAckq+pGVT0BTAT65thmGPCGqu4HUNXdnp9Jqrre8347sBuo4LPkF2LTPMhMt2mgjTE+F5TfBiIyBugN7FbVprmsHww8DAiQBtypqv43DIQIXPkSpG6Gr+52vqKrXnxmUoqLCOXNwa35dsUOHvtqFVe9Np8R3epxR9c6BAeWnn/fGFPKxANbvT6nADlvXPUBROQnIBAYparTvTcQkXZACLDBa/FTIvIY8D3wiKoez3lyERkODAeIj48nNTX1wq6mgMqs/IrQ4DLsj2oMOc6Znp7usxxnYzkshz9nsBznL9/CGBgLvA6My2P9JqCLqu4XkZ7AO5x54/YPQSEw4CN471KYeAMMmw1Eup3qnFzZvAodasfw+JTVvDgriemrd/LCdS1oVCXK7WjGGHcEAfWArkACMFdEmqnqAQARqQJ8BNyiqif7YY0EduIUy+/gNG48kfPAqvqOZz2JiYkaE+ODYdNUYcscqNONmIpVzlidmpqKT3Lkw3JYDn/OYDnOX75Njao6F8iz1FfVn09+hQcsxLkx+68yMTDoU8jOgAkD4Xjxe6AtNiKU129oxejBrdh16BhXvTafl79L4kSm9T02poTZBlTz+pzgWeYtBZiiqhmquglIwimUEZEo4FvgUVVdeHIHVd2hjuPABzhdNvzDrtVwKMVGozDGuKIgLcbnYigwLa+Vbn0td4bAOIJ6vE7kV7cS8dlA0i76Gxk1L3H1IY/z+aqhfXwon97anBe+38zL361n6opt/LtnXRpUKuvTHIXNHzJYDsvhJxYB9USkFk5BPBC4Icc2XwKDgA9EJA6na8VGEQkBvgDGqepk7x1EpIqq7hARAa4GVhXxdRRckud/IfUudzeHMaZUKrTCWEQuwSmMO+W1jStfy+Ulpg+EfEDgtEeI/HqoM6zbxX+DRn0gINDncc73q4aYGBh9SyVmrN7Jo1+s4qaPVnLXJXW555K6hASde99jf/jKwx8yWA7L4Q9UNVNE7gFm4PQfHqOqq0XkCWCxqk7xrLtcRH4HsnBGm9gnIjcCnYFYEbnVc8hbPSNQfCIiFXCeDVkG3OHbKzuLpBlQtZUzDr0xxvhYoRTGItIceA/oqar7CuOYPtG4LwcrtCcm5TuY/xL871aIrQcXPwjNroPAYLcTFtgVTSrTvlYM//76d179fj0zPX2Pm8aXczuaMeYCqOpUYGqOZY95vVfgQc/Le5uPgY/zOGa3wk9aCA7vgZTF0HWk20mMMaXUBQ9nICLVgc+Bm1Q16cIj+VhgiDOM292/Qv8PICjMmQjk1Vbw67uQcczthAVWvkwILw1I5L2b25B65AR93/iJF2eu43hmltvRjDEmf8mzALVh2owxrsm3MBaRCcACoIGIpIjIUBG5Q0ROfvX2GBALvOmZQWlxEeYtOgGB0PQauGOe83BeZCWY+hC80hx+erVYPaTXvXElZj3Qhb6JVXltdjJ9XvuJFSkH3I5ljDFnlzQdIqtAlRZuJzHGlFL5dqVQ1UH5rL8duL3QErlNBBr0cFosNs+DuS/ArP+DeS9Chzuh3XBnZAs/V65MMP+9PpHezasw8vOV9HvzZ/7auTb3da9HaJDv+1AbY8xZZZ6A5NnQ7Fqb7c4Y4xqbGSIvIlCrM9wyBW7/Hmr8xZlO+uVmMOsxOLzb7YQF0q1hJWY+0IVrWsbz5pwN9H51Psu2WuuxMcbP/PEznEizYdqMMa6ywrggEtrAoAlwx09OS/LPrzkF8rcPwYGt+e/vsnLhwTx/XQs+GNKWtGOZXPPmTzwzbQ3HMqzvsTHGTyTNcJ7xqNXF7STGmFLMCuNzUbkp9B8D9yyGZv1hyQfwaiJ8eTfsTXY7Xb4uaVCRmQ925vo21Xj7x41c+eo8lv6xP/8djTGmKKnCumnOt3QhZdxOY4wpxawwPh+xdaDvGzBiGbQZCqsmw+ttnOHedq50O91ZRYUF8+y1zfnwtnakn8ii/+ifeXqqtR4bY1y0Lxn2b7LRKIwxrrPC+EKUrwa9noP7V0LH+2D9d/BWJxg/ALYucjvdWXWpX4EZD3RmQNvqvDN3I71emceSLaVmNjFjjD9Jmu78rGeFsTHGXVYYF4aIinDZv+GBlXDJo7D1F3i/O4ztDRvnOF8T+qHIsGCeuaYZHw9tz/HMbPq/tYDHpyaTsv+o29GMMaVJ0gyo1NRpbDDGGBdZYVyYwqOhyz/g/lVw+VOwdz2M6wvvdXf6z/lpgdypXhwzHujMbR1rMX3NXrq98COjpqxm7+HjbkczxpR06Qdgy8/WjcIY4xesMC4KoRHwl3vgvuVw5X/hyG6YMNDpZrFyMmT7X3/eiNAg/q93Y74c1pJ+LeMZt2AznZ/7gRdnruPQsQy34xljSqrk70CzbJg2Y4xfsMK4KAWHQduhcO9S6Pc2ZGXAZ0Ph9baw9CNnQHs/UyUqlP/0b86sB7twSYOKvDY7mc7P/cDbP26wB/SMMYUvaQaUiYX41m4nMcYYK4x9IjAYWgyEuxbC9eMgpCxMuQdebQm/vA0Z6W4nPEOdChG8MbgV39zbiRYJ5Xlm2lq6PP8DHy/cQkZWttvxjDElQVYmJM+CepdDgM3IaYxxX75TQptCFBAAjftCoz6Q/D3MewGm/QPmPk9E5dYQEQ3BZZxxPEMiTr0PLusU03++z2V9YNH8VTaNL8eHt7Xjl437eG7GOv715SrenbeRBy+rz1XNqxIQYFO3GmPOU8oiSN9v/YuNMX7DCmM3iEC97s5r80+w4HUC9iTDvmNw4iicOAKZ59iKHBiao3Aue+p9sKeQ/vO9p9A+430EohEQHe1k9NK+diyT77iI2Wt38/yMddw3cRmj52zg71c0oFvDiohYgWyMOUdJ0yEgCOp0czuJMcYAVhi7r2ZHqNmRQ6mpxMTEnFqenQ0ZR53XicNOwZzhKZpPHMn9fW7LDu/2vPccJ+MoZOXdtzkanII6pjbE1HImM4mpAzG1kdg6XNqwEpc0qMjXK7bz31lJDP1wMa1rRPP3KxrQoXZskf9xGWNKkKQZUOMvEFbO7STGGANYYey/AgKc0S1CI4CKhXvsrAyv4vkoZHiK6eNpHNm+lrLHdsK+DbB7jTPMXLbXqBTBZQmIqU3f2Nr0TqzNrwfLM3ZtAPe+s4lG9eryjx4NaRpv/5MzxuRj/2bYswZa3ex2EmOM+ZMVxqVRYDCEl3deORyPa0tZ75brrEw4uBVSNzqvfRucn7tWE7j2Wy7KzuQigDA4sjWMzW9XYmlUTWrVb0Z0tUaeluc6ziQo1t3CGHNS0kznp/UvNsb4ESuMzdkFBjldKmJqAZeevu7PonkD7NtI0J5kwpNXUXZ/EhFL5sNSr+HdQiI8x6nj6Z5R+9T7shWsaDamtEmaDrH1nHuAMcb4CSuMzfnzLprrQihQG9h7+DjPzF7Lj78spabspH+tE3StkEb4oc2wcyWs/QayM08dJyTSqz9zbUJCK0GVehBZBSIqOTMKWuFsSjER6QG8AgQC76nqs7lscz0wClBguare4Fl+C/Avz2ZPquqHnuWtgbFAODAVuE/VR9NzHj8Mm+dBu+E+OZ0xxhRUvoWxiIwBegO7VbVpLusF54bdCzgK3KqqSws7qCk+4iJCeaxPC1Iurser36/n7iUphG8JZOjFtRnWvxaRwcCBPyB1k6e12dM9Y8dy+H0KEZpjIpHAUIis7BTKkZW9XlVO/xkaZQW0KXFEJBB4A7gMSAEWicgUVf3da5t6wEigo6ruF5GKnuUxwONAG5yCeYln3/3AaGAY8AtOYdwDmOaTi9o4x3kI2Ga7M8b4mYK0GI8FXgfG5bG+J1DP82qPc7NtXxjhTPGWEF2G5/q3YHjnOvx31jpe/X49Hy3YzF1d63LTRTUJi60DdD99p6wMDvyxmvIB6ZC2A9J2nv5z12rYMBuOHzrzhEHhuRfMOQvp0EhfXL4xhaUdkKyqGwFEZCLQF/jda5thwBueghdV3e1ZfgUwS1VTPfvOAnqIyBwgSlUXepaPA67GV4Vx0nQILQfVO/jkdMYYU1D5FsaqOldEap5lk77AOM9XcAtFpLyIVFHVHYWU0RRzdStG8Obg1qxMOcjzM9fx1NQ1vD9/E/d1r8d1rRMICvSagDEwmOxy1cH7AcDcHD8Mh3flUjx7XjuWO//zzTh65r4hEU4XjdMK51zeG+Mf4oGtXp9TOLPxoT6AiPyE091ilKpOz2PfeM8rJZflpxGR4cBwgPj4eFJTUy/oQgDQbMqvm05G9Ys5cjDtnHdPT08vnBwXyHJYDn/OYDnOX2H0Mc7rxntGYVwkN9kL5C9/YaUhR3wZePnquiz5owKvzf2DkZ+vZPQP67mzUzUuaxhLgKcbRIEzSDRERUNU49zXq8KJwwQc2U3AkV2n/ZQju5z3f/xKwJFdSNbxM3YvFx7L8eqdyKjemYwaF6NlKlzI5Z+30vC7URxz+JkgnG/tugIJwFwRaXahB1XVd4B3ABITEzUmv3+wFsS2JXB0D6FN+xB6HsdLzTnmu0ssh+Xw5wyW4/z59OG7IrnJXiB/+QsrTTkui4mhe4uafL9mNy/MXMfIr9fz0eJd/P2KBnRt4BSfhZchFqhx9k1U4djBM7ptZKYsJzTlJ0LXfeVsV6UF1O3uvBLaOsPe+UBp+t0oTjl8aBtQzetzgmeZtxTgF1XNADaJSBJOobwNp1j23neOZ3lCPscsGkkzQAKc/46MMcbPFEZhXJCbtjGnERG6N65Et4YVmbLcmUVvyNhFtK0ZzV8vqkp3XxY+IqfGda7Y8M/FR1JTCS1fHnaugOTvIPl7mP8yzHvRedCvdhfnf+51LoXy1c5yAmMuyCKgnojUwrm3DgRuyLHNl8Ag4AMRicPpWrER2AA8LSLRnu0uB0aqaqqIHPr/7d15eFXluffx752JDITMkJkQEsYgASQgkyKCaBWs2gq+9pRz1dqearW1k/q21mNHPbU9bU97fK21ek5b0IJDbDUgg1PRMlPmEOYkzFEQGUOe94+1IzsBmhSTvXbC73Nd+9rT2nv9EsKTO8++17PMbCTewXf/Avyy/b8UvBan3DJI0JkyRST8tEVhXA7cFTggZARwSP3F0loREcYNQ3L4xCVZPLt0F79YsJnbZ66j5M1dTBuez9TSbBJjQzMze56AkF3qXcZ93ZtZ3vrGmUJ5w8vedhn9ArPJEyB/FETH+pdZOhXnXL2Z3QXMxesffso5t87MHgaWOefKA89NMrP1wGngG865gwBm9j284hrg4cYD8YAvcWa5tlcJxYF3h2u9/v8J3233XYmIXIjWLNc2E++juHQzq8Zb+icawDn3ON4yP9cCVXjLtf1re4WVzis6MoLbRvbkpqG5PP3mJl5ae4Bvv7iWH/xlA9cPzmJ6WT6lecmY38uxxSbBgCnexTnYvylQJM+HJb+Bd/7LWx2j19gzbRephVpGTj4W59wreGNt8GMPBt12wL2BS/PXPgU8dY7HlwFnLcHZrjY3nu1Oy7SJSHhqzaoU01t43gF3tlkiuajFxURyy9BMvjihP6urDzFryU7KV9fy3LJq+mUmMm14Hp8ckktSvI+zyI3MvNaL7v1g1F1w8ijs+Ctsfs0rlBuLgJSCM0VywVjo0tXX2CK+qZwLSfnQvb/fSUREzklnvpOwZGaU5iVTmpfMt68bQPmqWmYt3clDL6/nR69u5BODsphWls/wghT/Z5EbxcRD8UTvAt5JS6oWeJdVM2HpkxARDT0vO1Modx+g2WS5OJw65p3Yk6Cr3QAAGjNJREFUY8ht+pkXkbClwljCXtcuUdw6Ip9bR+SztuYQs5bu5KWVtTy/sobeGQlML8vnxqG5pCbE+B21qdRCKCuEss9D/QnY+a43k7xlIbz2oHdJzPL6kouugsIrvNNfi3RG29/21hXvc7XfSUREzkuFsXQoJTlJfD9nEA9c258//303s5bs5Pt/2cCjFZu4uiST6cPzGFmYRkREmM1IRXXxVrEovBz4nncQ0paFXqG84WVY+XtvCavc4WcO4ssa4h38J9IZVFZAdAL0HON3EhGR81JhLB1SfEwUn740j09fmsemPR8wc8lOXlhZw8uraylIi+eW4fncPCyXjMQufkc9t27Z3kfKQ26D0/VQu+LMQXyLfgiLfgDxaVA4ni4p/SApDaJivQI7Ki5wHXvmsejgxwLXIVpnWaRFznn9xb3Ha8UWEQlrKoylw+ubmchDUwZy3zX9eHXtbmYu2cUjFRt5bN4mJg7owbSyfMYWpYffLHKjyCjIK/Mu4x+ADw/C1kUfFcoJa2df2PtaZNNCOTq26f2ziuzzbeddYo6fhMQkiIjyLpHREBF55v45L5GB7Zo9FhH8mGbFO7196+HQLrj8m34nERH5h1QYS6cRGx3JJ4fk8skhuVTtO8KzS3cye3k1r67dQ25KHLdcmsenLs0jMynMZ6wS0mDQzd6loYH39mwnJTEe6o/DqePedf2JwHXz+ye8g5zOev4c25w8CkfrAo8fa/p8/fGzYrXfWhoWVGhHNSu2zy6+uzU0QFSU9zqzwHVE0O3m1xFnDvY673MX8DppvcoK77p4kr85RERaoMJYOqWi7l35v58YwNev7su8dXuZtXQnj71Wyc/mV3Jlvx5ML8vj8j4ZREWGeYETEYGLTYbEEJ8C2blmhfIx3q87QHJiV2ioh4ZT0HA6cDtwOV3f9P45L6fh9Kmm9z96v/qm73n6HPtoqKfhxHGIjgaclxMHriHodtB14+2GhlZu37gdLW8vrbepArKHQGKm30lERP4hFcbSqXWJiuT6wdlcPzibHQc/ZNbSXfxpWTXzN+wls1ssnx6exy3D88hJjvM7angx81oqgvpBG1w3COWpus/jSF0dqWGQg7vDtDUn3Hx4AKqXwhX3+Z1ERKRFKozlotEzLYFvTe7HvRP7sGDDXmYu2cUvF27mlws3c3mfDKYNz2dC/+5Eh/ssskhHsvk1wGmZNhHpEFQYy0UnOjKCySVZTC7JYlfdUf60bBfPLavmi79fTkZiFz41LJdpw/PpqglBkY+vsgK6ZkLmYL+TiIi0SIWxXNTyUuO5d1Jf7p5QzOub9jNr6U4ef2MLv359CyN6JvG5cUVM6N+DyHBd0UIknNWf9NbrHniDVh8RkQ5BhbEIEBUZwVUDenDVgB7sPnSMPy2r5g/vbueO/11Obkoc/3JZT265NJ+keK0NLNJqO9+BE4ehz2S/k4iItIoKY5FmspLiuHtCMdMGp7Js90meXrydH76ykZ+9tpkbhuQwY1QBfTMT/Y4pEv4q50JkF+905yIiHYAKY5HziIowrh2UxbWDslhfe5hnFm/n+RXVzFyyk8sK0/jsqAImDlCbhch5VVZAr3EQk+B3EhGRVlHTl0grDMjuxiM3X8K790/gW5P7sbPuKF/8/XLGPbqIx9/YwvtHT/odUSS8HKiCui1ajUJEOhQVxiL/hJSEGP7tit688Y0rePy2YeSlxvHjVzcy8kcLuG/O39mw+7DfEUXCQ+PZ7lQYi0gHolYKkQsQFRnB5JJMJpdksnGP12bxwsoaZi3dxYheqcwItFmE/Zn1RNpLZQV0HwjJ+X4nERFptVb91jazyWa2ycyqzOys0xeZWb6ZLTKzlWb2dzO7tu2jioSnfpnd+NGNXpvFA9f2o/q9Y/zbH1Yw7tFF/Pr1Kuo+VJuFXGSOve+tSKHZYhHpYFosjM0sEvgVcA0wAJhuZgOabfZt4Dnn3BBgGvDrtg4qEu6S42O4Y1xv3vzmeJ74zDAK0hN4tGITl/1oAd+cvZp1tYf8jigdUCsmJmaY2X4zWxW43B54fHzQY6vM7LiZ3RB47mkz2xb0XGmbht6yEBrqtUybiHQ4rWmlKAOqnHNbAcxsFjAVWB+0jQO6BW4nAbVtGVKkI4mMMCYNzGTSwEw27fmAZ97ZzgsranhuWTVlBal8dlQBkwb20KmnpUVBExMTgWpgqZmVO+fWN9v0WefcXcEPOOcWAaWB90kFqoB5QZt8wzk3u12CV1ZAXCrkXtouby8i0l5aUxjnALuC7lcDI5pt8xAwz8y+DCQAV53rjczsDuAOgJycHOrq6v7ZvG3u2LFjyhFmOcIhQ1vlyIiBr1+ewx0jelC+Zh/PrtjDnX9cQfeuMXxqSA9uHNyDlBZOGtKZvh+dKUeItGZiojVuBl51zh1t43xnazgNm+dB8dUQEdnuuxMRaUttdfDddOBp59xjZnYZ8L9mVuKcawjeyDn3BPAEQGlpqUtNTW2j3V+4uro6lCO8coRDhrbOkQrcnd2dOycOZNHGfTy9eDu/emsXv3mnhimDs5kxqoCSnKR2z/FxKIcvWjMxAXCTmY0DKoGvOud2NXt+GvDTZo/9wMweBBYA9znnTjR/0wuZzIiqXUa3Y+9xJHs0J9vhD5hw+cNIOZQjnDMox4VrTWFcA+QF3c8NPBbsc8BkAOfcO2YWC6QD+9oipEhnERlhH516umrfBzyzeAdzVlQze3k1w3qmMGNUAZNLMtVmIf+Ml4GZzrkTZvYF4BngysYnzSwLGATMDXrN/cAeIAZvsuJbwMPN3/iCJjNWLIaIKLoOngJxyRf6NZ1XuPxhpBzKEc4ZlOPCtea371Kg2Mx6mVkM3sxDebNtdgITAMysPxAL7G/LoCKdTVH3RL53Qwnv3D+B71w3gANHTvDlmSsZ88hCfrlgMweOnDWBJxefFicmnHMHg2Z7nwSGNXuPTwMvOOdOBb1mt/OcAH6H17LRNirnQv5l7VIUi4i0txYLY+dcPXAX3mzDBrzVJ9aZ2cNmNiWw2deAz5vZamAmMMM559ortEhnkhQXzefG9GLR167gqRmX0jezG4+9VsmoHy3k3mdXsab2A/Tf6aLV4sREYEa40RS8cTrYdLxx+azXmJkBNwBr2yTteztg33qtRiEiHVareoydc68ArzR77MGg2+uB0W0bTeTiEhFhXNmvB1f268GW/Uf4n8Xbmb28mudX1lDUfTs3Dc3lk0NyyEyK9TuqhIhzrt7MGicmIoGnGicmgGXOuXLg7sAkRT1QB8xofL2ZFeDNOL/R7K3/YGYZgAGrgC+2SeDNgUUvVBiLSAelM9+JhKHeGV3596klfP3qvjz7ThUVG9/jkYqN/MfcjYwuSufmYblcPTCT2Ggd9d/ZtWJi4n68nuFzvXY73gF8zR+/8uyt20BlBaQVQXpRu7y9iEh7U2EsEsYSY6O5cXAPbh/fn20HPuT5FdU8v6KGe2atIrFLFNcNzuKmobkM65mC96m4iE9OHIFtb0LZHX4nERG5YCqMRTqIXukJfG1SX756VR/e3XqQ2SuqeXFlLTOX7KIgLd5rtRiaQ25KvN9R5WK07Q04fVKngRaRDk2FsUgHExFhjCpKZ1RROg9PrefVNbuZs6Kax16r9A7a653GTUNzuWZQJvEx+i8uIVJZAV26eStSiIh0UPqtKdKBde0SxacuzeNTl+axq+4oz6+oYc6Kar72p9V856W1XDvIa7UY0SuViAi1Wkg7aWiAynlQNAEi//GZHEVEwpkKY5FOIi81nnuuKubuCUUs3f4ec5ZX85c1u5m9vJrclDhuHJrLTUNz6JmW4HdU6Wz2rIYje7QahYh0eCqMRToZM6OsVyplvVJ5aMpA5q7bw5wV1fxy4WZ+sWAzwwtSuHlYLtcOyiIxVrN70gY2VQAGRRP9TiIi8rGoMBbpxOJiIrlhSA43DMmh9v1jvLCyhjnLq/nWnDV8t3wdkwdmctOwXEb1TidSrRZyoSorIK8MEtL8TiIi8rGoMBa5SGQnx3Hn+CK+dEVvVu56nznLqylfXcuLq2rJSorlk0NyuGlYLr0zuvodVTqSw7th9yqY8GDL24qIhDkVxiIXGTNjaH4KQ/NT+M51A5i/YS+zl1fz+Btb+PXrWyjNS+bmYblcf0k2SfFqtZAW6Gx3ItKJqDAWuYjFRkdy3SXZXHdJNvsOH+fFVTXMXl7Nt19cy8N/Xs/E/j24eVguY4vT/Y4q4apyLiTlQfcBficREfnYVBiLCADdu8Vyx7jefH5sIWtrDjNnRTUvrarhL2t2k5HYhav7pnLLyEhKcrrpLHviOXUcti6C0ltBPxMi0gmoMBaRJsyMQblJDMpN4oFr+7Nw4z7mrKhm1oo9/H7ZbnqlJzBlcDZTSrPVj3yx2/42nDqqNgoR6TRUGIvIecVERTC5JJPJJZlsq9nLuzUnKF9Vyy8WbubnCzZTktONqYNzuG5wFllJcX7HlVCrrIDoeCgY63cSEZE2ocJYRFolKS6a6WU9mF6Wz55Dx/nz32spX13LD17ZwA9f3UBZQSpTS3O4piSTlIQYv+NKe3PO6y8uHA/RsX6nERFpEyqMReSflpkUy+1jC7l9bCFb9x/h5dW7eWl1DQ+8sIYHX1rL5X0ymFKazcQBPYiP0TDTKe3bAId2wriv+51ERKTN6DeWiHwshRldPzoV9braw5SvrqV8VS0LNu4jLjqSiQN6MLU0m7HFGcRERfgdV9pKZYV3XTzJ3xwiIm2oVYWxmU0Gfg5EAk865358jm0+DTwEOGC1c+7WNswpImHOzCjJSaIkJ4n7JvdjyfY6ylfX8sqa3ZSvriU5PpprSrKYWppNWUEqETrTXsdWOReySqFblt9JRETaTIuFsZlFAr8CJgLVwFIzK3fOrQ/aphi4HxjtnHvPzLq3V2ARCX8REcbIwjRGFqbx0PUDebtqPy+tquXFlTXMXLKTzG6xXD84i6mlOQzM1vJvHc6HB6F6CYz7pt9JRETaVGtmjMuAKufcVgAzmwVMBdYHbfN54FfOufcAnHP72jqoiHRMMVERXNmvB1f268HRk/XM37CP8lU1PL14O795axuFGYHl3wZnU6jl3zqGqtfANUCfq/1OIiLSplpTGOcAu4LuVwMjmm3TB8DM/orXbvGQc66i+RuZ2R3AHQA5OTnU1dVdSOY2dezYMeUIsxzhkEE52i/HmLxYxuT15tCxfBZU1lGx/gA/n7+Z/5y/mQGZCUzun86kfml0T+zSrjk6kpba2cxsBvAfQE3gof9yzj0ZeO40sCbw+E7n3JTA472AWUAasBz4jHPuZKsCVVZA1x5eK4WISCfSVgffRQHFwBVALvCmmQ1yzr0fvJFz7gngCYDS0lKXmpraRru/cHV1dShHeOUIhwzK0f45UoHbc3pw+3g+Wv7tpVW1/HTRDn72+g5G9kpjSmk215Rkkhx/Zvm3cPl+hEpr2tkCnnXO3XWOtzjmnDtXBfsI8DPn3Cwzexz4HPDfLQY6fQqqFsCAqRChgylFpHNpzahWA+QF3c/lzKxEo2qg3Dl3yjm3DajEK5RFRFrUuPzby18ew8KvXc49E4rZe/g49z+/huE/mM/tzyylfHUtR0/W+x3VDx+1swVmdBvb2S6YeU3dVwKzAw89A9zQqhfvfAdOHNbZ7kSkU2rNjPFSoDjwsVsNMA1ovuLEi8B04Hdmlo7XWrG1LYOKyMWhMKMrX7mqD/dMKGZd7WFeWlXDy6t3M3/DPuJjIinL78b4AdmMKU6nMD3hYjhwrzXtbAA3mdk4vImJrzrnGl8Ta2bLgHrgx865F/HaJ953zjX+pVEd2E8T52p/i/v7i8RGxPBeymDwoaUlXFpplEM5wjmDcly4Fgtj51y9md0FzMXrb3vKObfOzB4GljnnygPPTTKz9cBp4BvOuYPtGVxEOrfg5d/uv6Y/S7bX8fLqWl7fuJfXy9cBkJUUy+iidMYWpzOqdzoZ5+lLvgi8DMx0zp0wsy/gzQBfGXiup3OuxswKgYVmtgY41Jo3PWf72843oNdYUjPzWnh1+wiXVhrlUI5wzqAcF65VPcbOuVeAV5o99mDQbQfcG7iIiLSp4OXf6upyOOJiebvqAG9X7Wf+hr3MXl4NQL/MRMYUpTO6OJ0RvVI7y1n3WmxnazYR8STwaNBzNYHrrWb2OjAEmAMkm1lUYNb4XC1yZztQBQeroOwLF/aViIiEuU7xW0NELi75afHcmpbPrSPyOd3gWF97mLeq9vPXqgP8z7s7ePLtbURHGkPzUxhTlM6Y4nQG5SQRFdkhDxZrsZ3NzLKcc7sDd6cAGwKPpwBHAzPJ6cBo4FHnnDOzRcDNeD3LnwVeajHJ5rnetZZpE5FOSoWxiHRokRHGoNwkBuUm8aUrijh+6jRLt9fxdtUB/lp1gJ/Or+Sx1ypJjI3issI0xhanM7oonV4dpD+5le1sd5vZFLw+4jpgRuDl/YH/Z2YNeAdb/zhoNYtvAbPM7PvASuC3LYaprIDuAyClZ9t9gSIiYUSFsYh0KrHRkYwtzmBscQYAdR+eZPEWr0h+a/MB5q3fC0B2UixjAkXy6KJ00ruGb39yK9rZ7sc7+2jz1y0GBp3nPbfirXjRKuYaYMdiGPXl1r5ERKTDUWEsIp1aakIM112SzXWXZOOcY2fdUa8/efMB5q7by3PLvP7k/lndGFOUxuiidEb0SiMuJtLn5OHFTn4ADfVapk1EOjUVxiJy0TAzeqYl0DMtgf8zoienGxzrag/x1mZvRvmZxTv4zVvbiImMYGjP5EB/cgaDcpKIjAj/tov2ZCc+gLgUyB3udxQRkXajwlhELlqREcYluclckpvMneOLOHbS60/+a9UB3q46wE/mVfKTeZV0i43ist5pjCnOYExROgVp8X5HDzk7+QEU3wgRmkkXkc5LhbGISEBcTCTj+mQwro/Xn3zwyAkWbzn4UX/y3HVef3JOcpyfMf3RUK/VKESk01NhLCJyHmldu3D94GyuH+z1J+84eKY/ebHf4ULMxSRC7wl+xxARaVcdclFPEZFQMzMK0hO4bWRPHv/MML/jhNzp5AKIS/Y7hohIu1JhLCIiIiKCCmMREREREUCFsYiIiIgIoMJYRERERARQYSwiIiIiAqgwFhEREREBVBiLiIiIiAAqjEVEREREADDnnD87NvsA2OTLzptKBw74HQLlCLcMoBzNKUdTfZ1ziX6HCBWN2WdRjqaUI7wygHI016ox289TQm9yzl3q4/4BMLNlyhFeOcIhg3IoR2ty+J0hxDRmK4dydJAMynHuHK3ZTq0UIiIiIiKoMBYRERERAfwtjJ/wcd/BlKOpcMgRDhlAOZpTjqbCJUeohMvXqxxNKUdT4ZAjHDKAcjTXqhy+HXwnIiIiIhJO1EohIiIiIoIKYxERERERwKfC2Mwmm9kmM6sys/t8yvCUme0zs7V+7D+QIc/MFpnZejNbZ2b3+JQj1syWmNnqQI5/9yNHUJ5IM1tpZn/2McN2M1tjZqv8XJbLzJLNbLaZbTSzDWZ2mQ8Z+ga+D42Xw2b2FR9yfDXw87nWzGaaWWyoMwRy3BPIsM6P74MfNGY3yaFx++wsGrPP5NCYfSZHxxyznXMhvQCRwBagEIgBVgMDfMgxDhgKrA31voMyZAFDA7cTgUqfvhcGdA3cjgb+Boz08ftyL/BH4M8+ZtgOpPu1/6AczwC3B27HAMk+54kE9gA9Q7zfHGAbEBe4/xwww4evvwRYC8TjrQM/Hyjy++ckBP/mGrPP5NC4fXYWjdlncmjMdh17zPZjxrgMqHLObXXOnQRmAVNDHcI59yZQF+r9Nsuw2zm3InD7A2AD3g9TqHM459yRwN3owMWXozLNLBf4BPCkH/sPJ2aWhFcM/BbAOXfSOfe+v6mYAGxxzu3wYd9RQJyZReENcrU+ZOgP/M05d9Q5Vw+8AdzoQ45Q0pj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       "Find user logs for this run at: <code>/content/wandb/run-20210922_062136-12qqnlo7/logs/debug.log</code>"
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       "Find internal logs for this run at: <code>/content/wandb/run-20210922_062136-12qqnlo7/logs/debug-internal.log</code>"
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       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>0.71817</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.32325</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.49988</td></tr><tr><td>valid_loss</td><td>0.90863</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr></table>"
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       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>▁▅▅▆▇▇████</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▇▇▆▅▆▅▃▄▄▄▃▃▃▂▄▃▃▃▃▃▂▃▂▂▁▂▂▁▂▂▃▂▂▁▂▂▂▁▁</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>██▇▆▆▅▅▅▄▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁</td></tr><tr><td>valid_loss</td><td>█▄▃▃▂▂▁▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
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       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">vocal-glitter-43</strong>: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/12qqnlo7\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/12qqnlo7</a><br/>\n",
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       "\n",
       "                Tracking run with wandb version 0.12.2<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">swift-wave-44</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/3fj5gqmc\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/3fj5gqmc</a><br/>\n",
       "                Run data is saved locally in <code>/content/wandb/run-20210922_062202-3fj5gqmc</code><br/><br/>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.019165</td>\n",
       "      <td>1.515511</td>\n",
       "      <td>0.531225</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.543177</td>\n",
       "      <td>1.471329</td>\n",
       "      <td>0.552717</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.299206</td>\n",
       "      <td>1.239401</td>\n",
       "      <td>0.620032</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.119941</td>\n",
       "      <td>0.981235</td>\n",
       "      <td>0.681671</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.986218</td>\n",
       "      <td>0.982784</td>\n",
       "      <td>0.682887</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.882408</td>\n",
       "      <td>0.982510</td>\n",
       "      <td>0.683293</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.764272</td>\n",
       "      <td>0.917734</td>\n",
       "      <td>0.710057</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.659821</td>\n",
       "      <td>0.928926</td>\n",
       "      <td>0.706002</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.559555</td>\n",
       "      <td>0.889057</td>\n",
       "      <td>0.725872</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.490166</td>\n",
       "      <td>0.889080</td>\n",
       "      <td>0.724250</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7ncqn8tNF2x64BVghIss8y54AagCo6ps4F+0ewAac6doG5jdAy5oxdG9ShbcSN9KvTXUqlYkoSH5jTACKjY2lffv2NGnShMjIyD+HSQB069aNN998k4YNG1K/fn3atm3rYlJTFBo3bkxKSgpxcXFUrVqV/v37c/XVV9O0aVNatWpFgwYNzrj93XffzcCBA2nYsCENGzakZcuWRZTcmGLiWDL88A9Y8h6UqgC9xkCzvsV+2ERO8jMrxU84H7edqY2S7aaPgnisWwNmrd7Ny9+t59lrm57tbowxAWTSpEk5Lg8PD2fGjBk5rtuyZQsAFSpUYOXKv4bVPvLIud97Zty1YsVfN/5VqFCBBQsW5NjuyJEjgDNDycnfgcjISCZPnlz4IY0pbrIy4Zfx8P3TkHYI2gyBjsMgstjMBV9gflHq165Qiv5tavDx4m1s2OPbO9yNMcYYY0wBJS2Fd7rAVw9AxQZwV6JzL0EJLorBTwpjgKFd6hIVGsxzM9a6HcUYY4wxJjAd3QfT/uYUxYd3wHVvw8DpUKWJ28mKhN8UxrGlwxnS8Ty+W7ObnzftdzuOMcaYfDh1IoySLZDO1QSgrExY9Da82hKWTYJ298J9S6DZjc7cwQHCbwpjgNvb16ZK2Qienb7GLkDGGOPnIiIi2L9/f0Bcr1WV/fv3ExFhN4ibEmjbIhjbEaY/AlWbwZB5cMUzEFHW7WRFzq8mDo4MC+bhy+vx6NTlfLV8J1c3r+Z2JGOMMbmIj48nKSmJvXv3Ftkxs7KyCHLpTviIiAji4+NdObYxheLIHpg1En6bBGWqwQ3vQeNrA6qHODu/KowBrrsgnnd/2szzM3/n8saVCQ8JdjuSMcaYHISGhlK7du0iPaa/PAnSmGItM8N5Yt3sZyH9GFz8IFzyCISXdjuZ6/xqKAVAcJDwRI+GbEtOZcLCP9yOY4wpAUqXdi72O3bs4IYbbsixTceOHVmyZElRxjLGlDRH9hC+chKs+RJ2LHPm//W3oUZb58PYS+GbxyG+JdyzALqOsqLYw+96jAE61KvIJXUr8OoP67mhZTzlIkPdjmSMKQGqVavG1KlT3Y5hjCmJkpbCxzdTKmXHqcvDSkO56lC+uudrDc/3nq+lKhXNgzJSdsGsEbD8YyfHjR9Cw6sDethETvyyMAYY3r0hV776I2/M3sDwHg3djmOM8SPDhg2jevXq3Huv81yhUaNGERISwuzZszlw4ADp6emMHj2anj17nrLdli1buOqqq1i5ciWpqakMHDiQ3377jQYNGpCamurGqRhjSoJfJ8BXD0KZKhy+4X+UjakIB7fBoW1w8A/P9384N7mlHTx12+BwKBd/auHsXUCXqQbB51CuZabDz2/BnOcg8zh0eBQufgjCos7tnEsovy2MG1Ury3Ut4nlv/hZuaVeT+Gj7BzTG78wYBrtW5N2uIKo0dSaRP4M+ffrwwAMP/FkYT5kyhZkzZzJ06FDKli3Lvn37aNu2Lddccw2SS2/ImDFjiIqKYs2aNSxfvpwLLrjAt+dhjCn5MtNh5hOwaCzUvhR6v09GGhATA9Va5LxN2mE4lOQUzCcL50PbnOJ53Uw4uufU9hIMZatl622u/levc7l4CM1ltpTNP8L0R2HvGqh7OXR7DmLP8+mPoKTx28IY4OHL6/HV8h28+O06XuqT4HYcY4yfaNGiBXv27GHHjh3s3buX6OhoqlSpwoMPPkhiYiJBQUFs376d3bt3U6VKlRz3kZiYyNChQwFo1qwZzZo1K8pTMMYUd0f2wv9ug63zoN190PXvTs9uWvKZt4soCxGNoHKjnNenpzmF8yFPT7N34bx1HqzYDpp16jalK582XKPUhrmw7ksoXxP6TYZ63WzYRD74dWFcrXwkt19cmzFzNnLHxbVpElfO7UjGGG959OwWpt69ezN16lR27dpFnz59mDhxInv37mXp0qWEhoZSq1Yt0tLSXMtnjCnBdvwKk2+GY/vg2rHQvI/v9h0aARXOd145yUx3nkh3slj27nXe+Rv8/jVkniAsOBw6Dof290NopO/ylXB+XRgD3N3xPCYv+oNnp69h4p1tcv1Y1BgTWPr06cOgQYPYt28fc+fOZcqUKVSqVInQ0FBmz57N1q1bz7h9hw4dmDRpEp07d2blypUsX768iJIbY4q13z6GL4dCVAW4fSZUK+JPtINDIbqm88pJVhYc3cPBw0eJjrNhEwXld9O1ZVc2IpShXeoyf+N+5qwruknkjTH+rXHjxqSkpBAXF0fVqlXp378/S5YsoWnTpowfP54GDRqccfu7776bI0eO0LBhQ0aMGEHLli2LKLkxpljKzIBvnoDPBkNcKxg8p+iL4vwICoIyVdDIaLeTFEt+32MM0L9NTT6Yv4Xnpv9Oh7oVCQ6yXmNjDKxY8deNfxUqVGDBggU5tjty5AgAtWrVYuXKlQBERkYyefLkwg/pAyLSDXgFCAbeUdXnsq1/CejkeRsFVFLV8p51twFPedaNVtUPPMtbAu8DkcB04H4NhGc7G3M2ju6HqQNgcyK0vst5XHKwTSVbEvl9jzFAWEgQj3VrwNrdKUxdus3tOMYYU2REJBh4HegONAL6icgpd+2o6oOqmqCqCcCrwKeebWOAkUAboDUwUkROdiONAQYBdT2vbkVwOsYUP7tWwNsd4Y+foecb0ON5K4pLsDwLYxEZJyJ7RGRlLuvLiciXIvKbiKwSkYG+jwndm1ShRY3y/GfWOo6dyCiMQxhjjD9qDWxQ1U2qegKYDPQ8Q/t+wEee768AZqlqsqoeAGYB3USkKlBWVRd6eonHA70K7xSMKaZWTIV3LnOGUQycAS36u53IFLL89Bi/z5l7Eu4FVqtqc6Aj8KKIhJ17tFOJCE/2aMjuw8d598fNvt69MaYAAukTdz841zjA+6OyJM+y04hITaA28EMe28Z5vs9zn8YEpKxM5ylxn9wBVZs744nj7T6EQJDnGGNVTRSRWmdqApQRZ7qI0kAyUChduq1qxXBF48q8OXcjfVvXoGKZ8MI4jDHmDCIiIti/fz+xsbElfpYYVWX//v1EROQyeb7/6QtMVdVMX+xMRAYDgwHi4uJITs5jftYikJqaajksR6HmkLSDlP5mKKF//Eha0/4c6zAC0kMhn/stST+LkpQjv3xx891rwDRgB1AG6KOafeZp33msWwO+W5PIf79fzz96NSmswxhjchEfH09SUhJ797ozS0xWVhZBQUV3e0RERATx8fFFdrwcbAeqe72P9yzLSV+cT/G8t+2Ybds5nuXx2Zaftk9VHQuMBUhISNCYmJiCJS8EycnJWA7LUWg5dq+C/90Eh7bD1a8Q0XIABf2zuMT8LEpYjvzyRWF8BbAM6AycB8wSkR9V9XD2hr7ofYgOhuuaV2LSz1u5tnE0tWLPbdJqf/lLxnL4VwbLceYc5cqVo1w5dx64k5qaSmRk0U5Wn5KSUqTHy2YxUFdEauMUr32Bm7I3EpEGQDTgPTXHTOBZrxvuLgeGq2qyiBwWkbbAz8CtODftGRO4Vn8Bn90N4aVhwNdQo43biYwLfFEYDwSe89zAsUFENgMNgEXZG/qq9+HxK0sxfdU+3lywk7G3tjr75PjPXzKWw78yWA7L4S9UNUNE7sMpcoOBcaq6SkSeBpao6jRP077AZO8p1zwF8D9wimuAp1X15F9Z9/DXdG0zPC9jAk9WJsx+Fn78tzM/cZ8JULaq26mMS3xRGP8BdAF+FJHKQH1gkw/2m6sKpcMZcul5vDhrHYu3JHNhrcD5n6QxJvCo6nScuYa9l43I9n5ULtuOA8blsHwJYOPRTGBLPQifDob1M6HFLXDlixBi9y8FsvxM1/YRzkdz9UUkSUTuEJEhIjLE0+QfwEUisgL4HnhcVfcVXmTHnZfUoXLZcJ6dvsYf7ho3xhhjTHGydy283Rk2fu8UxNe8akWxydesFP3yWL8DZ9xakYoMC+bhy+rz2CfLmb5iF1c2s489jDHGGJMPv38Nn94FoRFw25dQ8yK3Exk/USyefJeb61vGU79yGZ6f+TsnMgptIgxjjDHGlARZWTD7nzD5JqhwvjM/sRXFxkuxLoyDg4RhPRqwdf8xJv681e04xhhjjPFXaYfh4/4w9zlofpPzJLtyrk7FaPxQsS6MATrWq0j782P57/frOZSa7nYcY4wxxvibfRvgnS6wbiZ0+xf0egNCi3baR1M8FPvCWEQY3r0hB46lM2bORrfjGGOMMcafrJsJb3eCY/vh1s+h7RAo4U/tNGev2BfGAE3iynFtizjGzdvM9oOpbscxxhhjjNtUIfEFmNQHoms544lrd3A5lPF3JaIwBnj48noAvPjtWpeTGGOMMcZVx4/AlFvhh9HQ9Aa4fSaUr+F2KlMMlJjCOD46ioHta/HZr9tZteOQ23GMMcYY44bkTfDuZfD7V3D5aLjubQiLcjuVKSZKTGEMcE/H8ykXGcpzM353O4oxxhhjiljo1rkwtiOk7ISbP4WL/mbjiU2B+OKR0H6jXGQof+tcl398tZq56/Zyab2KbkcyxhhjjDdV0CzIygTNdL5mZZy+7M+vWc7609Zlnbr9tkWUnvMsVGwIfSdCTG23z9QUQyWqMAa4pW1NPpi/hX9OX8PF51cgOMj+UjTGGBPgsjIhPRUy0pyv6amQkQrpaZB+LJfl+W9TLv0EiOZdvKqn0C0kJ+peSXjvtyGsVKEdw5RsJa4wDgsJ4tEr6vO3j37l01+S6N2qutuRjDHGGN9b9hGl1swAycxWtHoKWe/iNvPE2R1DgiE0ynl0cmgkhEQ6X0MjneKzVEUICScjI4vgyFIgQRAU7Gx3ylfP8qCQ05fl2DYk5+1PaRsCQUF/LQsrxdHwGoRbUWzOQYkrjAGualaVd37azIvfruOqZtWIDAt2O5IxxhjjO7tXwed3ExpVAUpXhJAIp4CNivEqYD3LQiL+Kma9C9tTlkd4FcBe2wSH5ivO0eRkwmNiCvmk8yE52e0EppgrkYWxiPBE9wb0GbuQcfM2c2+n892OZIwxxvjOd3+H8LIcuvlboqvVcTuNMSVGiZqVwlubOrF0bViZMXM2su/IcbfjGGOMMb6x5SdYPxMueRCNKO92GmNKlBJbGAMM696A1PRMXv1+vdtRjDHGmHOnCrNGQJlq0GaI22mMKXFKdGF8fqXS9L2wOhN//oNNe4+4HccYYwpMRLqJyFoR2SAiw3Jpc6OIrBaRVSIyybOsk4gs83qliUgvz7r3RWSz17qEojwncw7WTIPtS6HTE84YYGOMT5Xowhjgga71CAsJ4vlv7FHRxpjiRUSCgdeB7kAjoJ+INMrWpi4wHGivqo2BBwBUdbaqJqhqAtAZOAZ867XpoyfXq+qyIjgdc64y052xxRUbQPN+bqcxpkTKszAWkXEiskdEVp6hTUdPr8MqEZnr24jnpmKZcO7qcB7frNrF0q12t6oxplhpDWxQ1U2qegKYDPTM1mYQ8LqqHgBQ1T057OcGYIaqHivUtKZw/TIekjdCl5EQXCLvnTfGdfn5L+t94DVgfE4rRaQ88AbQTVX/EJFKvovnG4M61Gbiz1t55us1fHL3RYg9HtIYUzzEAdu83icBbbK1qQcgIvOAYGCUqn6TrU1f4D/Zlj0jIiOA74FhqnraXcoiMhgYDBAXF0eyH0yFlZqaGpg5Thyl/OxnyazWipSKbf6clixgfx5+nMMfMliOs5dnYayqiSJS6wxNbgI+VdU/PO1z6q1wVVRYCA9dVo9hn67gm5W76N60qtuRjDHGV0KAukBHIB5IFJGmqnoQQESqAk2BmV7bDAd2AWHAWOBx4OnsO1bVsZ71JCQkaIwfzFObnJxMQOaY+w4c20dQ30nExMa6lyMXlsO/MliOs+eLz2LqAaEiMgcoA7yiqrn1LrvW+9C5TinqxEby7NeruaBKKKHBzigSf/lLxnL4VwbLYTn8xHbA+/Gd8Z5l3pKAn1U1HdgsIutwCuXFnvU3Ap951gOgqjs93x4XkfeARwojvPGRo/tg3ivQ4Cqokf0DA2OML/miMA4BWgJdgEhggYgsVNV12Ru63fvw1NWNuf39JXyz/gi3XVQL8J+/ZCyHf2WwHJbDTywG6opIbZyCuC/Op3TePgf6Ae+JSKixzrkAACAASURBVAWczopNXuv74fQQ/0lEqqrqTnHGlfUCcr2HxPiBxBecRzx3Gel2EmNKPF/MSpEEzFTVo6q6D0gEmvtgvz7XqX4l2tWJ5ZXv13M4LT3vDYwxxkWqmgHchzMMYg0wRVVXicjTInKNp9lMYL+IrAZm48w2sR/AMwyuOpD9puiJIrICWAFUAEYX9rmYs5S8GRa/Cy1ugYr13E5jTInni8L4C+BiEQkRkSicG0PW+GC/PiciPNGjIclHT/DW3I1uxzHGmDyp6nRVraeq56nqM55lI1R1mud7VdWHVLWRqjZV1cle225R1ThVzcq2z86etk1U9WZVtYne/dUPoyEoBDoOz7utMeac5We6to+ABUB9EUkSkTtEZIiIDAFQ1TXAN8ByYBHwjqr67cdyTePL0TOhGu/8uJmdh1LdjmOMMcbkbMcyWDkV2t0DZe2mcWOKQn5mpchzFnFVfQF4wSeJisAjl9dnxopdvPjtOp7oUj3vDYwxxpii9t0oiIyG9ve7ncSYgFHin3yXk+oxUdx2UU0++SWJdXuOuh3HGGOMOdXGH2DTbOjwKESUczuNMQEjIAtjgPs61aVsRCj/+Gaj3YhnjDHGf2RlwayRUK4GXHin22mMCSgBWxiXiwrlX9c3Y+2eY/R9ayH7jpz20CdjjDGm6K36FHYth85PQUi422mMCSgBWxgDdGtShZeuq8+mfUe48c0FbD9oN+MZY4xxUcZx+P5pqNwUmvZ2O40xAce1wjgo7SDsXA7paW5FAKB9nWgm3NGGvUeO03vMfDbutVmLjDHGuGTJe3BwK3QdBUEB3XdljCt88eS7sxJ0eBu8dQlIEETXhkoNnVfFBs7X2LoQElYkWVrVimHy4LbcNm4Rvd9cwPjbW9Mkzm52MMYYU4TSDkPi81C7A5zfxe00xgQk1wrjzNh6cMNo2PM77F0De9bA2ulwch76oBCIOQ8qNYCKDf8qnGPqQHCoz/M0rlaOKXe145Z3F9Fv7ELeua0VberE+vw4xhhjTI7mvwrH9ju9xSJupzEmILlWGGtwODS5/tSF6Wmwf/2pxfLO5bB6GqBOm6BQqFDX07Pc6K/COaY2BAWfU6Y6FUsz9e523PzOz9w6bhFv3tySTg0qndM+jTHGmDyl7IIFr0HjayGupdtpjAlYrhXGOQqNgCpNnZe3E8dg3zqnUN67ximcty9x7tw9KTjceY58xYan9jKXr1mgcVpVy0Uy5a52DHhvMYPGL+HFG5vTMyHORydojDHG5GDuvyDzBHT+P7eTGBPQ/Kswzk1YFFRLcF7ejh+BvWv/6l3e+ztsnQcrpvzVJjQKKtTzGr/s6WUuVz3Xj6piS4czaVAb7vhgCQ98vIyUtAxubluzEE/QGGNMwNq3AZZ+AK1uh9jz3E5jTEArHoVxbsJLQ3xL5+Ut7ZBTMO9Z81cv88bZ8NtHf7UJKw0V6xNR+3Lo8vhpRXKZiFDG396aeyf+wlOfr+RQajr3dDwPsXFfxhhjfOn7v0NoJFz6mNtJjAl4xbswzk1EOaje2nl5O5bsKZhXO73L238h6qd/wpFtcPXLp93UFxEazJu3tOSR//3GCzPXcjg1nWHdG1hxbIwxxje2LYY106DjcCht97QY47aSWRjnJioGarZzXgCqpM4YSeSiV+DILuj9gdML7SU0OIiXbkygbEQobyVu4lBqOs9c25TgICuOjTHGnANV+G4klKoI7e51O40xhgB/8h0ipLZ9AK7+rzPU4v0ekLL7tGZBQcLTPRtzX6fzmbx4G0M/+pUTGVkuBDbGGFNirP/WuS/m0schvIzbaYwxBHphfFLL26DfR7BvPbzb1fmajYjwyBX1ebJHQ75esZM7xy/h2IkMF8IaYwKNiHQTkbUiskFEhuXS5kYRWS0iq0RkktfyTBFZ5nlN81peW0R+9uzzYxEpmicqGUdWJnw3ypmbv+UAt9MYYzysMD6p3hUw4Ctnarh3L4Nti3JsNqhDHf51fVN+Wr+XW95dxKHU9CIOaowJJCISDLwOdAcaAf1EpFG2NnWB4UB7VW0MPOC1OlVVEzyva7yW/wt4SVXPBw4AdxTmeZhsln/s3O/S+f8K5aFVxpizY4Wxt7iWcOcsiIyGD66GNV/l2KzPhTV47aYLWJ50kL5jF7I35XgRBzXGBJDWwAZV3aSqJ4DJQM9sbQYBr6vqAQBV3XOmHYpzB3FnYKpn0QdAL5+mNrlLT4MfnoFqLaCR/diN8Sd53nwnIuOAq4A9qtrkDO0uBBYAfVV1am7t/F5MHbhjFky6EabcAt2fh9aDTmvWo2lVSoeHcNeHS+n95nwm3NmG+OgoFwIbY0q4OGCb1/skoE22NvUARGQeEAyMUtVvPOsiRGQJkAE8p6qfA7HAQVXN8NrnaU8yEpHBwGCAuLg4kpOTfXNG5yA1NbXY54j4ZSxRh5M43OV5Mg4edC2HL1kO/8pgOc5efmaleB94DRifWwPPR33/Ar71TSyXlaoAt30JU2+H6Y/AoSToMvK0J+h1qFeRCXe2ZuB7i7lhzAIm3Nma8yvZDRTGmCIXAtQFOgLxQKKINFXVg0BNVd0uInWAH0RkBXAoPztV1bHAWICEhASNiYkplPAFkZycTLHOkXoAloyB87tStvmV7uXwMcvhXxksx9nLcyiFqiYCeZX6fwM+Ac748V2xElYK+kyElgNh3svw+RDIOHFas5Y1Y/j4rnZkZCm931zA8qRz++vfGGOy2Q5U93of71nmLQmYpqrpqroZWIdTKKOq2z1fNwFzgBbAfqC8iIScYZ+mMPz0svMQqq6j3E5ijMnBOc9jLCJxwLVAJ+DCPNoWv4/lLvo/IsJiiFrwIukHkkjpMea0aXUqh8M7/Rpy98dr6Dd2IS9dV59WNcr5NkcR8Ycc/pDBclgOP7IYqCsitXGK177ATdnafA70A94TkQo4Qys2iUg0cExVj3uWtweeV1UVkdnADThjlm8Dviia0wlgh5Lg5zeh2Y1QpanbaYwxOfDFAz5eBh5X1ay8nghXbD+Wu2IEVDqP0C+HEvP5TdB/KpStekqTmBj47N5Ybn73Z+6b+jtv3HQBXRtV9m2OIuAPOfwhg+WwHP5CVTNE5D5gJs744XGqukpEngaWqOo0z7rLRWQ1kAk8qqr7ReQi4C0RycL5hPA5VV3t2fXjwGQRGQ38CrxbxKcWeOb8EzQLOj3pdhJjTC58URi3wrm4AlQAeohIhucGj5KjRX8oUxmm3OZM59Z/KlRqcEqTKuUimHJXOwa8t4i7Jizlxd7N6dXitPtZjDGmQFR1OjA927IRXt8r8JDn5d1mPpBj16RnaEVrn4c1OduzBpZNgjZ3Q3RNt9MYY3JxztO1qWptVa2lqrVwpv65p8QVxSed3xUGfA2ZJ2Dc5bB1/mlNYkqFMfHONlxYK5oHPl7G+AVbijymMcYYP/Pd3yGsNFzysNtJjDFnkGdhLCIf4UzDVl9EkkTkDhEZIiJDCj+eH6qW4EznVqoSjO8Fq07/G6BMRCjvD2xN14aVGPHFKl77YT1Oh44xxpiAs3U+rJsBFz8ApWLdTmOMOYM8h1Koar/87kxVB5xTmuIiuibc8S181Bf+NwAOPwvt7jmlSURoMGNubsljU5fz72/XcSg1nSd6NCSvcdjGGGNKEFWYNRJKV3GGURhj/JovxhgHpqgYuPUL+OROmDkcDm+Hy/5xylzHocFBvNi7OWUjQnj7x80cSk3nn9c1IzjIimNjjAkIv38FSYvg6lcgzB4CZYy/s8L4XIRGwo3j4ZthsOA1SNkJvcZASPifTYKChFHXNKZcZCj//WEDKWkZvNw3gfCQYBeDG2OMKXSZGc7Y4gr1IOFmt9MYY/LBCuNzFRTsPDa6bBx8NxJSdkPfiRBZ/s8mIsJDl9enbGQoo79ew5EPlvDWLS2JCrMfvzHGlFjLJsD+9c7DooLtem9McXDOs1IYQMS5qeK6d2DbzzCumzORezZ3XlKH529oxrwN+7j5nZ85dCzdhbDGGGMK3YljMPufUL0NNDj3Rz8bY4qGFca+1Kw33DzVGW/8zmWwe9VpTW5sVZ03+l/Ayu2H6TN2AXtS0lwIaowxplAtfAOO7IKuf3c6T4wxxYIVxr5WpyMMnAGo03O8OfG0Jt2aVGXcgAv5I/kYvd9cwLbkY0Wd0hhjTGE5uh/mvQL1e0DNdm6nMcYUgBXGhaFKE2eu47LV4MPrYMXU05pcXLcCE+5sw4GjJ7jhzfms23PUhaDGGGN87scX4cQR6DLS7STGmAKywriwlK8Ot3/jjC/75A6n9yDbQz4uqBHNlCHtUIVbP1zB24mbyMyyB4EYY0yxdWArLH4bEvpDpQZupzHGFJAVxoUpMhpu+RQaXwuzRsCMxyEr85QmDaqU5euhl3BR7fI8M30N/cYu5I/9NrTCGGOKpdnPgARBx+FuJzHGnAUrjAtbSDhcPw7a3guL3nKelJeeekqTimXCefHa+vy7d3PW7DxMt1cS+WjRH/YYaWOMKU52LoflU6DNECgX53YaY8xZsMK4KAQFQbdn4YpnYc2XML4XHEs+pYmIcEPLeL55sAMJ1csz/NMV3P7+YvYctlkrjDGmWPhuFESUc6bvNMYUS1YYF6V290Lv92DHLzDuCmcsWjZx5SOZcEcbRl3diPkb93P5y4l8tXyHC2GNMcbk26Y5sPF76PCIM4zOGFMsWWFc1BpfC7d8Bkd2w7uXOR+9ZRMUJAxoX5vp919CzdhS3DfpV/720a8cPHbChcDGGGPOKCsLZo2EsvFw4SC30xhjzoEVxm6odTHcPhOCQuG97rDxhxybnVexNJ8MacfDl9VjxoqdXP5SIrPX7inisMYYY85o9eewcxl0fhJCI9xOY4w5B1YYu6VSQ7hzFkTXgom9CVv7RY7NQoKD+FuXunx+b3vKR4Uy8L3FDP90BUePZxRtXmOMK0Skm4isFZENIjIslzY3ishqEVklIpM8yxJEZIFn2XIR6ePV/n0R2SwiyzyvhKI6nxIn4wR8/zRUagzN+uTd3hjj16wwdlPZajBwOtRoR6mZD8KySbk2bRJXjmn3XcxdHeowefEfdHslkUWbk3Ntb4wp/kQkGHgd6A40AvqJSKNsbeoCw4H2qtoYOHnn1zHgVs+ybsDLIlLea9NHVTXB81pW2OdSYv3yARzYDF1HQVCw22mMMefICmO3RZSDm6aQUf0i+Pwe+OXD3JuGBjO8R0M+Huw8YrTP2AU8O30NaemZuW5jjCnWWgMbVHWTqp4AJgM9s7UZBLyuqgcAVHWP5+s6VV3v+X4HsAeoWGTJA8HxFJj7L6h5MdS9zO00xhgfCMmrgYiMA64C9qhqkxzW9wceBwRIAe5W1d98HbREC4si5ep3iJl5L0y7DzQLWt6Wa/PWtWOYcX8Hnvl6DWMTNzFn7R7+c2MCTeLKFWFoY0wRiAO2eb1PAtpka1MPQETmAcHAKFX9xruBiLQGwoCNXoufEZERwPfAMFU9nv3gIjIYGAwQFxdHcrL7n1Klpqb6TY7UhS8TeXQvh64cS+aBA67l8Jefh+XwnwyW4+zlWRgD7wOvAeNzWb8ZuFRVD4hId2Asp1+4TV5CIqDvR/DxzfDlUNBMaHV7rs1Lh4fwz+uacnnjyjw+dTm9Xp/H/V3qcnfH8wgJtg8CjAkgIUBdoCMQDySKSFNVPQggIlWBD4HbVDXLs81wYBdOsTwWp3Pj6ew7VtWxnvUkJCRoTExM4Z5JPiQnJ+MPOQ4krSXy13egUU/KNersWg5/+XlYDv/KYDnOXp4VlKomArmW+qo6/+RHeMBCnAuzORuhEdB3ItS9Ar56EBa9necmnepX4tsHO9C9aVVenLWO699cwMa9R4ogrDGmCGwHqnu9j/cs85YETFPVdFXdDKzDKZQRkbLA18CTqrrw5AaqulMdx4H3cIZsmAKIXPQqZKRB5xFuRzHG+FB+eowL4g5gRm4r7WO5fOa47BVKZ9xH2PRHOHo0hePNB+S5/d+vqMlFNUrx3KzN9HglkaGX1qTPBVUIEjn7HC7xhwyWw3L4icVAXRGpjVMQ9wVuytbmc6Af8J6IVMAZWrFJRMKAz4DxqjrVewMRqaqqO0VEgF7AykI+j5Jl/0bCV34ELQdAhfPdTmOM8SGfFcYi0gmnML44tzb2sVwBcvT/CKYOpNTcv1MqItx5al4e+rWPoUvTGjz+yXJe+H4L87ak8ELv5sSVjzz7HC7whwyWw3L4A1XNEJH7gJk444fHqeoqEXkaWKKq0zzrLheR1UAmzmwT+0XkZqADECsiAzy7HOCZgWKiiFTEuTdkGTCkaM+smJv9jDMP/aWPu53EGONjPimMRaQZ8A7QXVX3+2KfAS8kDHq/D1Nvh5lPQFYmtB+a52aVykYwbsCFTF68jdFfrabbS4mMvKYx118QhxSw99gY4z5VnQ5Mz7ZshNf3CjzkeXm3mQBMyGWf7g2KLe72roWVn5LWcgiRZSq7ncYY42PnfJeWiNQAPgVuUdV15x7J/Ck4FG4Y5zxGetb/wU8v5WszEaFf6xrMuL8DDauW5ZH//cbgD5ey78hpN50bY4wpiMQXIDSKtAvudDuJMaYQ5FkYi8hHwAKgvogkicgdIjJERE5+9DYCiAXe8DxBaUkh5g08waFw3TvQ5Ab4bpRzUc6nGrFRfDS4LU/2aMjcdXu5/KVEvlm5q/CyGmNMSbZvPaz8BC68A40MjOE8xgSaPIdSqGq/PNbfCdifzoUpOASufct5qtIPoyErCzrmb2xbcJAwqEMdLq1fkYemLGPIhKVc1yKOkdc0plxkaCEHN8aYEiTx387UmhcNhRNuhzHGFAab8La4CA6BXmOg+U0w51mY/Syo5nvzepXL8Nk97RnapS5f/LaDbi8n8tP6fYUY2BhjSpD9G2HFFGd++dL2AEFjSiorjIuToGDo+Rok3Ow8hvSH0QUqjkODg3josnp8evdFRIYFc/O7PzPyi5WknrBHShtjzBn9+CIEhzm9xcaYEssK4+ImKBiueRUuuBV+/Dd8//cCFccAzauXZ/rQSxjYvhYfLNhKj//+yC9/uPM4U2OM8XvJm+C3yU5vsc1EYUyJZoVxcRQUBFe94lykf3oJZo0ocHEcERrMyKsbM2lQG05kZHHDmPm8MPN3jmdk5b2xMcYEkh9fhKAQaH+/20mMMYXMCuPiKigIrvwPXDgI5v8XZj5Z4OIY4KLzKjDjgUu4/oJ4Xp+9kV5v/8qEhVs5YQWyMcbAgS1Ob3HLAVCmittpjDGFzArj4kwEerwAbYbAwtfhm2FnVRyXjQjlhd7NmXRnGyqXCeOpz1fS+cU5TFm8jYxMK5CNMQHsx/+ABMHFD7idxBhTBKwwLu5EoNtz0PZe+PlNmP7oWRXHABedX4H3+jfh/YEXElMqjMc+WU7X/8zls1+TyMw6u30aY0yxdfAPWDYRLrgNylZzO40xpgj45JHQxmUicMUzzvCK+a+CZkKPF533Bd6V0LF+JS6tV5Hv1uzhP7PW8eDHv/H67I080LUuPZpUJSjIHi1tjAkAP70EiPUWGxNArDAuKUTgsn84N4j89BJkZcJVL59VcezsTrisUWW6NKjEN6t28dKsddw36VcaVNnAA13rcUXjyohYgWyMKaEOJcEvH8IFt0C5eLfTGGOKiBXGJYkIdBkJEuxM5aaZcPWrZ10cAwQFCT2aVuWKxlX4avkOXv5uPUMmLKVJXFkeuqwenepXsgLZGFPy/PSS8/Xih9zNYYwpUlYYlzQi0Pkpp+d47nPO46N7vubMf3wOgoOEnglxXNm0Kp8v28Er36/j9veX0KJGeR66rB4Xn1/BCmRjTMlweAf8Mh4SboLy1d1OY4wpQlYYl0Qi0Gm4cyf1nGednuNeY865OAYICQ7ihpbx9EyoxtSlSbz6/XpueXcRrWvF8NDl9WhbJ9YHJ2CMMS766WXQLLjkYbeTGGOKmBXGJVnHx51hFD+MdsYcX/sWBPvmnzw0OIh+rWtw3QVxfLx4G6/9sIG+YxfS/vxYHrqsHi1rxvjkOMYYU6RSdsHS96F5X4iu6XYaY0wRs+naSroOj0LXUbByKnx6J2Sm+3T34SHB3NquFomPdeKpKxuydlcK149ZwG3jFrE86aBPj2VMoBKRbiKyVkQ2iMiwXNrcKCKrRWSViEzyWn6biKz3vG7zWt5SRFZ49vlfsbFQjnmvQFYGXPKI20mMMS6wHuNAcPGDzg15s/7P6Tm+YRwEh/r0EBGhwdx5SR1ualOD8Qu28tbcjVzz2jy6NqzMQ5fVo1G1sj49njGBQkSCgdeBy4AkYLGITFPV1V5t6gLDgfaqekBEKnmWxwAjgVaAAks92x4AxgCDgJ+B6UA3YEbRnZkfStkNS8Y5vcUxtd1OY4xxgfUYB4r2Q+GKZ2HNNPjfAMg4USiHiQoLYcil55H4WCcevqweizbvp8d/f+SeiUtZtzulUI5pTAnXGtigqptU9QQwGeiZrc0g4HVPwYuq7vEsvwKYparJnnWzgG4iUhUoq6oLVVWB8UCvojgZvzb/v5B5wsYWGxPArMc4kLS71+k5/uZx+N9t0Pt9CAkvlEOViQjlb13qcutFtXj3x02Mm7eFGSt3cU3zatzfpS51KpYulOMaUwLFAdu83icBbbK1qQcgIvOAYGCUqn6Ty7ZxnldSDstPISKDgcEAcXFxJCcnn9OJ+EJqamqh5JBj+yi/+B1O1O/JUYmGPI5RWDkKynL4Xw5/yGA5zl6ehbGIjAOuAvaoapMc1gvwCtADOAYMUNVffB3U+EjbIc7sFNMfgY9vgT4fFlpxDFAuMpSHLq/PwPa1eStxEx/M38KXv+3g2hbx3N+lLjViowrt2MYEkBCgLtARiAcSRaTpue5UVccCYwESEhI0Jsb9m2qTk5MplBxLXoLME4R3fZLwfOy/0HIUkOXwvxz+kMFynL38DKV4H2fsWW6641yQ6+L0LIw591imULUeBFe9BOtnwuT+kJ5W6IeMLhXGsO4NSHysE7e3r81Xy3fQ+cU5DP90OdsPphb68Y0pxrYD3pPpxnuWeUsCpqlquqpuBtbhXJNz23a75/sz7TNwHN0Hi9+BJtdDhbpupzHGuCjPwlhVE4Ez9YH3BMarYyFQ3jN+zfizVrfD1a/Ahlkw+SZIL5ritGKZcJ66qhGJj3Wif5safLJ0O51emMOIL1ay+3DhF+jGFEOLgboiUltEwoC+wLRsbT7H6S1GRCrgDK3YBMwELheRaBGJBi4HZqrqTuCwiLT1fOp3K/BFkZyNP1rwmnMN7PCo20mMMS7zxRjj3Maw7czeMJDGqxWLHLWvIaxLGqW+H0bG+OtJueptUjMokhyhwP2XVOPG5rG8uyCJST//wceL/+CGhCr0be4fH7kE9O+G5fAbqpohIvfhFLnBwDhVXSUiTwNLVHUafxXAq4FM4FFV3Q8gIv/AKa4BnlbVkz+8e3A+EYzEmY0iMGekOJYMi96GJtdBxfpupzHGuKxIb74LqPFqxSXHJUOgTFlCP7+HmBl3kdKwL2VKl3Ke+qTqvFCv91l5vM+hfa7bKDGq/KdKFiPLnWDxpn2s/e0w85ZD3UplaFytLJXLRuDMruo1xepp061me1+g9bmvO5ohlKrfCaom+OzBKGcj4H9H/TRHUVLV6ThTqnkvG+H1vQIPeV7Ztx0HjMth+RLgtPtGAs6C1+HEUestNsYAvimM8zP+zfizhJuc2So+H0KZLT+6EqEc0BWhS0gQWQpZe4G9kCkQJE756pSseuqGmu199vXnoBTAj6MhrDRUbwO1LnZe1Vr4fB5oY4wLjiXDz29Bo55QqaHbaYwxfsAXhfE04D4R+f/27jy6qvrc//j7yQQkQOYwJIGEUeYwqwwKCnWEtrYI3tra2mp/69p6tbdOvXrVVVtLW62drrVo1baAiKig1nkEFAiDjDIImASQKYAICASe3x/7AIeITcAk+yR8XmudlXP2GfbnRPnmOc/5fveeTHAIoV2R+WtSn/S6HAqHsmvzelJT08DiAAt+mkU6qdG3K99fE48Pit9d5eUkpTRn+qKNTJpbwpINu2iUEMfFPVsxbkAb+rVN55RO0lW5iK6iqN6xYQ3pu5bB+pnw0Sx47a7gjsQUyB8ABYOgYAi07gMJSSefR0TCNedBOLAbzrkp7CQiEiOqc7i2SQSLOrLMrIzgLEqJAO7+IMHXexcBawgO1/bd2gortax5Kw5VNIIY+Jq6aaMErhjYhisGtmHphl1MmlvCs4s2Mm3BBjrkNGVs/3wu65NHespJFKSVi+kqimtPyYb8rwdzDwE+3RoUyB/NCorl138ebE9oEimUIx3l3L61egg8EakB+3bCew9Cl0uhRbew04hIjKiyMHb3cVXc78B/1lgikUq656Zyz9d6cNtFXXh+8SYmzi3h58+vYPyLK7mwR0vG9m/Dme0yTq2LfDKaZkO3rwYXgD3bowrlWfDGLwCHhMaQ1z8oktsOCq4nNq7dbCJycuY8CPt3wVB1i0XkGJ35TuqNlEYJjOmfz5j++azY9AmT55YwbeEGnl20kXZZKVzeP5/L+uaR1bSOurUpmdB1VHCBYL5iybtBkbz+HXjzXsAhvhHk9QuK5ILBQaGcpBObiITms13w3p+h88XQqmfYaUQkhqgwlnqpS6vm3DW6O7dc2IUXlmxi8rwSfvmvD/jNyysZ2a0l4/q34ez2mcTF1XIXOVpyBpxxcXCB4KvaknePzVF+5zfw9niISzy+UM4fAEkpdZdT5HQ356GgONbcYhGpRIWx1GtNkuK5rG8el/XNY/Xm3UyaW8q0hWU8v3gTbTKSubx/Pt/sl0dOsxCmMjRJg84XBhcI/hCXzIGPZgbF8sz7g2I5LiFYwFcwOFjQl38mNGpae7ncoWI/HNwbHKbquJ974eCeyM+o7Qf30vhwIuT3hMyOkNkeEpvUXkaR2vLZJ8EJL87H9AAAHFBJREFUPTpdCK2Lwk4jIjFGhbE0GB1bNOOOS7ty0wWdeWnZx0yaW8KvX1rJ/a+s4rwuOYwb0IYhHbOJr8sucrTGqdBpZHAB2L87qlCeBbN/DzPvCw6d17r30aNexCVkw8GPT66QrWq7Hz657AmNSa74DN47ssEgNR+yOgSFclZHyOwQ/GyeW+XCRpHQzPsrfLZT3WIROSEVxtLgNE6MZ3RRLqOLcvlw66c8Ma+UqfPLeGnZZnLTmnB5/3zG9MunZWrIC+IaNYOO5wcXCIrX0jlBN3n9LHj3zzDrAdKq81oWFxxGLikZEpODqRmJTYLrKVmRbckneEz0zy+4PzEZ4uIo31xGxuHtsG01bF8T+bk6KO4P7jmWJTE56ChndTq+aM7sULudcJGq7P8UZv8ROo6E3D5hpxGRGKTCWBq09tlNue2iLvxkZCdeWb6ZyXNLue+VVfzu1VUMPyPoIp/TKZuE+LiwowaFaPvhwQWC7m7ZXD7duIqm6dn/vqhNaFT7XdrEZMjIg1a9jt/uDrs3HSuUt60JfpYVw9JpHHd86GatK3WZOwa3U/MhLr5284vM+yvsK4dzbg47iYjEKBXGclpolBDPJT1bc0nP1ny0fQ+T55XyZHEZr64opmXzxozpn8/l/fPJTYuhebNJydDuXA6k9YyJY0t/ITNo3jq4tDvn+PsOfgblayMFc1SnecnU4FBZR8Q3CrrMR6ZjRHeam1SrZy7y7x3YA7P/AO3PCxa/ioicgApjOe20zUzh5gvO4MYRnXhtxRYmzS3hD6+v5g+vr+acTtmMG9CG4WfkhB2zYUhsDC26Bpdo7rBna1SXOVI0b14GHzwPfujYY1Oyj3WWMzvSqMIgWYe7k5M072HYux3OvSXsJCISw1QYy2krMT6OC7q35ILuLSkt38uTxaU8UVzKtX+fT3azRozqnsW1w5LJaa6Tc9Q4M2iaE1wKBh1/X8UB2LE+qmCOTM/44HnYux0d2E5O2oG9weLWdsOCwyOKiHwBFcYiQH5GMjeO7MyPz+vImyu3MmluCY+8u4HH525kdFEuPxjSjs4tm4Ud8/SQkATZnYJLZft2smPbZtLTYmB6xV0tw04g1TX/b8E3FJpbLCJVUGEsEiUhPo7zu7bg/K4teP/DjUxbWs6U4jKmzi9jaKdsrhnSjkEdMmv/9NNyYk3S8JTD0CyG51xLbDm4D2Y9AIVDoe1ZYacRkRgXA0vxRWJTfnpj7hrdndm3DOe/R3Zi+cZP+NbDc7jo9zOZtqCMAxUneSxgEal78x+DTzfDOZpbLCJVU2EsUoX0lCSuG96RWbcMY/xlPak4dJgbp7zP0PFv8OBbH7Jr38GwI0oDZmYXmNlKM1tjZp+r7szsKjPbamaLIpfvR7YPi9q2yMw+M7OvRu571MzWRd3XME8Bd/AzmPU7aDv483PZRUROQFMpRKqpUUI8YyKnmH5z1VYmvLOWe//1AX94bTVjB7Thu4MKyEvX0RKk5phZPPAnYARQBswzs+nuvrzSQ59w9+uiN7j7G0BR5HUygDXAy1EP+am7T6218LFgwePBMba//lDYSUSknlBhLHKSzIxhnXMY1jmHpRt2MeGdtTw2ez2Pzl7PRT1a8YMhhfTMi4HFYdIQDADWuPtaADObDIwGKhfGVfkG8C9331vD+WJXxX6YeT+0ORsKhoSdRkTqCRXGIl9C99xUfje2NzddcAaPzl7PpDklzHh/IwMLM7hmaDuGdc4hLk4L9eSU5QKlUbfLgIEneNxlZjYUWAXc4O6lle4fC9xXads9ZnYH8Bpwi7vvr/yiZnYNcA1Abm4u5eXlp/YuatC+ffuqlaPR4n+Qsnsjn5z3Kyp27AgtR21TjtjLEQsZlOPUqTAWqQGt05pw20Vd+NHwDjwxr5RHZq7j6seKaZ+dwveHtONrvXNpnKhTHkutmAFMcvf9ZnYt8Bgw/MidZtYK6AG8FPWcW4GPgSTgIeBm4O7KL+zuD0Xup6ioyDNi4AyM5eXlVJmjYj8s+AvkD6R5r0tr5XTp1cpRB5Qj9nLEQgblOHXVWnxXjcUfbczsDTNbaGaLzeyimo8qEvuaNU7k+0Pa8dZNw3hgbBFNkuK5ddoSBv/qdR54dTXlew6EHVHqlw1AftTtvMi2o9x9e1S3dwLQt9JrjAGedveDUc/Z5IH9wN8Ipmw0HIsmwidlcM5NtVIUi0jDVWVhHLX440KgKzDOzCqd35X/Aaa4e2+Cr+z+XNNBReqTxPg4RhflMuO6wUz6wZn0zEvj/ldXcfa9r/Gzp5ewduunYUeU+mEe0NHMCs0siWB8nR79gEhH+IhRwIpKrzEOmHSi51hwQO6vAktrOHd4Kg7AO/dBbj9of17YaUSknqnOVIrqLP5woHnkeiqwsSZDitRXZsZZ7TM5q30ma7bsZsI763iyuIyJc0sY0aUFPxjajn5t03XCEDkhd68ws+sIpkHEA4+4+zIzuxsodvfpwI/NbBRQAZQDVx15vpkVEHSc36r00v80s2zAgEXAD2v5rdSdxZNhVwlccp+6xSJy0qpTGFdn8cedwMtm9iMgBTj/RC9UnxdyKMfplaE2cmQkwE3D8rh6QA5TFnzMlIWbeXn5Zrq3asq3B7RmWMcM4k+wUK+h/j7qe4664u4vAC9U2nZH1PVbCeYMn+i56wnG8Mrbh3/+0Q3AoYPw9m+gdW/ocMI/QyIi/1ZNLb4bBzzq7r81s7OAv5tZd3c/7tRg9XYhh3KcdhlqM0dGBvwsvyU3XFjBU/PLeHjmOm56dhX5GU24elAh3+yXT0qjY/80G/rvo77mkBi0+AnY+RFcOF7dYhE5JdVZfFfl4g/gamAKgLu/CzQGsmoioEhDlZyUwJVnFfDaT87lwW/1JadZY+6csZyz732d8S9+wJZPPgs7okj9cagi6Ba36gWdvhJ2GhGpp6rTMT66+IOgIB4LXFHpMSXAecCjZtaFoDDeWpNBRRqq+Djjgu4tuaB7S+Z/tIMJ76zlwbc+5K/vrGV0US7f6JHBmeqQivx7S56EHetg7ER1i0XklFVZGFdz8cdPgL+a2Q0EC/GucnevzeAiDVHftun0bduXj7bv4ZGZ65hSXMbU+WX0aVPCuAFtuKRna5ok6XjIIsc5VAFv/xpa9oDOOlqoiJy6as0xrsbij+XAoJqNJnL6apuZwl2ju3PDiE48/s4qnl26jZ9OXczdzy3na71zuWJgG85o2bzqFxI5HSybBuUfwuX/ULdYRL4UnflOJIalJSfxrf6t+dHIbsxbv4NJc0uYPK+Ux9/9iN5t0iJd5FYkJ+mfspymDh+Ct8ZDTjfofHHYaUSknqvWme9EJFxmxoDCDO6/vIg5t57H7Zd0ZfdnFdw0dTED73mNO55dyopNn4QdU6TuLXsatq8OznIXpz9pIvLlqM0kUs+kpyRx9eBCvjeogOKPdjBxzrEuclF+GlcMaMMlvdRFltPA4cNBtzi7C3QZFXYaEWkA9PFapJ4yM/oXBF3kubedxx2XdOXT/RXc9FTQRb79maUs36gusjRgy5+BbSvhnJ+qWywiNUItJZEGIC05ie8NLuS7kS7ypDklPFFcyt/f+4he+Wn8h7rI0tAcPhwciSKrM3T9athpRKSB0EdskQbkSBf5vkgX+X8v7creSBd5wD2v8T/PLGHZxl1hxxT58j6YAVuWR+YW6xCGIlIz1D4SaaDSkpP47qBCrjq7gPkf7WDi3BKeLC7jH++V0CsvlSsGBsdFjj79tEi9cGRucWZH6Pa1sNOISAOijrFIA2dm9CvI4L4xRcy97XzuvLQr+w4e4uanljDwF6/xs6eXsHSDushSj6x8ATYvhaE/VbdYRGqUWkUip5HU5ESuGlTId84uYEHJDibOKWXq/DL+OSfoIo8b0IZLe6mLLDHMHd76FWS0g+6XhZ1GRBoYdYxFTkNmRt+2Gfx2TC/m3nY+d43qxmcHD3PLtCUMuOdVblMXWWJU4rpX4ePFQbc4Xh/gRKRmaVQROc2lJifynbML+PZZbVlQspNJc0uYtqCMiXNK6JEbzEW+tFfrsGOKgDtN5v4B0gugx5iw04hIA6TCWESAI13kdPq2Tef2S7ry7KINTJxTwq3TlvDz55Yz8oxMrhxs9M5Pw8zCjiunowWPk7BlCYz6o7rFIlIrNLKIyOekNknk22cVcOWZbVlYupOJc0p4bvFGnl68hQ45TRnTL4+v9c4ju1mjsKOeFszsAuABIB6Y4O73Vrr/KuDXwIbIpj+6+4TIfYeAJZHtJe4+KrK9EJgMZALzgSvd/UAtv5VTs/tjeOG/YcUMKlr2JqHX2LATiUgDpcJYRL6QmdGnTTp92qRz/eDWzC7bx5TiMn7xwgeMf3Elw8/IYUy/fM7tnE1CvJYs1AYziwf+BIwAyoB5Zjbd3ZdXeugT7n7dCV5in7sXnWD7r4D73X2ymT0IXA38X01m/9LcYeE/4OWfQcV+OP9OPul8BRnxiWEnE5EGSoWxiFRLSqN4Lu/fhsv7t2HNlk95sriUpxZs4OXlm8lu1oiv98nlm33z6ZDTNOyoDc0AYI27rwUws8nAaKByYVxtFsyFGQ5cEdn0GHAnsVQYl6+DGdfDureg7SC49PeQ1QHKy8NOJiINmApjETlpHXKacutFXfjvr3TmzZVbmVJcyoR31vGXt9bSt206Y/rlcXHP1jTVYd9qQi5QGnW7DBh4gsddZmZDgVXADe5+5DmNzawYqADudfdnCKZP7HT3iqjXzK38gmZ2DXANQG5uLuV1UZQerqDxor/R5L378LhE9g27h/3dx4LFQXk5+/btq5scVVAO5YjlDMpx6vRXS0ROWWJ8HCO6tmBE1xZs2f0ZzyzcwBPzSrn5qSXcNWM5F/doxZj++fRrm64Fe7VrBjDJ3feb2bUEHeDhkfvauvsGM2sHvG5mS4BqHYvP3R8CHgIoKiryjIyMWoge5eOlMP062LgQOl2IXfxbUlJzSYl6SHl5ObWeoxqUQzliOYNynLpqFcZVLfyIPGYMwVdxDrzv7ldUfoyINFw5zRpzzdD2/GBIOxaW7uTJ4lJmvL+JJ+eXUZiVwjf75XFZnzxaNG8cdtT6ZgOQH3U7j2OL7ABw9+1RNycA46Pu2xD5udbM3gR6A08BaWaWEOkaf+4161TFfnj71zDzfmicBt94BLp9HfRhSkTqWJWFcXUWfphZR+BWYJC77zCznNoKLCKxLXrB3u2XdOWFJR8zpbiU8S+u5DcvreTczjmM6ZfH8DNakJSgBXvVMA/oGDmKxAZgLMfmBgNgZq3cfVPk5ihgRWR7OrA30knOAgYB493dzewN4BsER6b4DvBsnbybykreg+k/gm2roNc4+MovILn+dJdEpGGpTse4Ogs/fgD8yd13ALj7lpoOKiL1T3JSAt/om8c3+uaxbtseps4PTkH9w39sISMlia/1zmVMv3w6t2wWdtSY5e4VZnYd8BLBt3aPuPsyM7sbKHb36cCPzWwUwTzicuCqyNO7AH8xs8MEZzq9N6qpcTMw2cx+DiwEHq6zNwWwfze8djfM/Suk5sG3noIO59dpBBGRyqpTGFdn4UcnADObRTBw3+nuL1Z+oVAWclQhViaFK0dsZVCOms+RGgdX98/hqr7ZvLd+J88s3sJjs9fz8Mx1dG/VlFE9cvhKl0yaVbFgL1Z+H3XJ3V8AXqi07Y6o67cSfGtX+XmzgR5f8JprCRofdW/1KzDjv+CTDTDwWhh+OzTS0UxEJHw1tfguAegInEswV+1tM+vh7jujH1TnCzmqIVYmhStHbGVQjtrNcWlWJpf2a8/2T/fzzKKNTJlXyi9eXstvX1/PRT1a8c1+eZxZmElc3OfnmMbK70NOwZ7t8OItsGQKZHWGq1+G/HBqcxGRE6lOYVzlwg+CLvIcdz8IrDOzVQSF8rwaSSkiDVJm00ZcPbiQ7w0qYMmGXUwpLuXZRRt5euEG8jOa8M2++VzWN4/ctCZhR5Uvwx2WPgX/ugk++wTOuRmG/AQSdOZEEYkt1SmMq1z4ATwDjAP+Flng0QlYW5NBRaThMjN65qXRMy+N/7m4Ky8tCxbs3ffKKu5/dRWDO2Rxef98RnRtEXZUOVm7yuC5G2H1S5DbF0b9EVp0DTuViMgJVVkYV3Phx0vASDNbDhwCflrp8EEiItXSODGe0UW5jC7KpbR8L0/OL2NqcSnXTVxIWnIigwpTGd41l0EdsmiZqkO/xazDh2H+I/DKneCH4Cu/DOYTx8WHnUxE5AtVa45xNRZ+OHBj5CIiUiPyM5K5cUQnrj+vI7M/3MZT88t4c+UWnl+2DQjOwDe4QxaDOmQxsF0GzRsnhpxYANi2Gqb/GEpmQ7tz4dIHIL0g5FAiIlXTme9EJObFxxlDOmYzpGM227ZvZ/P+BGat2cbMNduZPK+ER2evJz7O6JWXyqBIody7TRqNEtSdrFOHDsKsB+Ct8ZDYGEb/GYqu0Ik6RKTeUGEsIvVKnBndWqfSrXUq1wxtz/6KQyws2RkplLfxpzfW8IfX19AkMZ4BhRlHO8pntGx2wqNcSA3ZuBCe/RFsXgJdR8OFv4ZmmhMuIvWLCmMRqdcaJcRzZrtMzmyXyU9GdmbXvoPMWbv9aKF8zwsrAMhMSeLsDlkM7pDJoA5Z5KUnh5y8gTiwF978Jbz7R0jJgcv/CV0uCTuViMgpUWEsIg1KapNERnZrychuLQHYtGsfs9ZsZ3akUJ7x/kYACjKTGdQhi8EdsjirfSZpyUlhxq6f1r0dzCXesQ76fAdG3A1N0sJOJSJyylQYi0iD1iq1ydHTUrs7a7Z8ysw125i1ZhvPLtrIP+eUYAY9clM5u31QKPcrSKdxouYnf6F9O+GV22HB45BeCN+ZAYVDw04lIvKlqTAWkdOGmdGxRTM6tmjGdwcVcvDQYRaX7WTm6mDqxYR31vLgWx+SlBBH/4L0ox3lbq1Tidf85MCK5+D5n8CeLXD2j+HcWyFJ01JEpGFQYSwip63E+Dj6ts2gb9sMrj+/I3v2VzB3XfnR+cnjX1zJeFaS2iSRs9tnHi2U22aefoWgHa6AKd+G5c9Ci+4wbhLk9gk7lohIjVJhLCISkdIogWFn5DDsjBwAtu7ez+wPg2kXM1dv419LPwY4LU9RHV++ClZugeG3w6DrIV7HjBaRhkeFsYjIF8hu1ujoWfjcnfXb9wbzk1dvY3bY4eqYJyTDD2dCdqewo4iI1Jq4sAOIiNQHZkZhVgpXntmWB6/sG3acOncorUBFsYg0eCqMRURERERQYSwiEtPM7AIzW2lma8zslhPcf5WZbTWzRZHL9yPbi8zsXTNbZmaLzezyqOc8ambrop5TVJfvSUQkVmmOsYhIjDKzeOBPwAigDJhnZtPdfXmlhz7h7tdV2rYX+La7rzaz1sB8M3vJ3XdG7v+pu0+t1TcgIlLPqGMsIhK7BgBr3H2tux8AJgOjq/NEd1/l7qsj1zcCW4DsWksqItIAqGMsIhK7coHSqNtlwMATPO4yMxsKrAJucPfo52BmA4Ak4MOozfeY2R3Aa8At7r6/8oua2TXANQC5ubmUl5d/mfdSI/bt26ccyhHTOWIhg3KcOhXGIiL12wxgkrvvN7NrgceA4UfuNLNWwN+B77j74cjmW4GPCYrlh4Cbgbsrv7C7PxS5n6KiIs/IyKjN91Et5eXlKIdyxHKOWMigHKdOUylERGLXBiA/6nZeZNtR7r49qts7ATh6LDkzaw48D/zM3d+Les4mD+wH/kYwZUNE5LSnwlhEJHbNAzqaWaGZJQFjgenRD4h0hI8YBayIbE8CngYer7zI7shzzMyArwJLa+0diIjUI+bu4ezYbDewMpSdHy8L2BZ2CJQj1jKAclSmHMfr7O7NansnZnYR8DsgHnjE3e8xs7uBYnefbma/JCiIK4By4P+5+wdm9i2CbvCyqJe7yt0XmdnrBAvxDFgE/NDdP60ih8bs4ynH8ZQjtjKAclRWrTE7zMK42N37hbJz5YjpHLGQQTmUo77kqCux8n6VQzliPUcsZFCOU8+hqRQiIiIiIqgwFhEREREBwi2MHwpx39GU43ixkCMWMoByVKYcx4uVHHUlVt6vchxPOY4XCzliIQMoR2XVyhHaHGMRERERkViiqRQiIiIiIqgwFhEREREBQiqMzewCM1tpZmvM7JaQMjxiZlvMLLQD25tZvpm9YWbLzWyZmV0fUo7GZjbXzN6P5LgrjBxReeLNbKGZPRdihvVmtsTMFplZcYg50sxsqpl9YGYrzOysEDJ0jvwejlw+MbP/CiHHDZH/P5ea2SQza1zXGSI5ro9kWBbG7yEMGrOPy6Fx+/NZNGYfy6Ex+1iO+jlmu3udXggOUv8h0A5IAt4HuoaQYyjQB1ha1/uOytAK6BO53gxYFdLvwoCmkeuJwBzgzBB/LzcCE4HnQsywHsgKa/9ROR4Dvh+5ngSkhZwnHvgYaFvH+80F1gFNIrenEJysoq7ff3eCs8QlAwnAq0CHsP8/qYP/5hqzj+XQuP35LBqzj+XQmO31e8wOo2M8AFjj7mvd/QAwGRhd1yHc/W2Cs0SFxt03ufuCyPXdBKdyzQ0hh/uxs14lRi6hrMo0szzgYmBCGPuPJWaWSlAMPAzg7gfcfWe4qTgP+NDdPwph3wlAEzNLIBjkNoaQoQswx933unsF8Bbw9RBy1CWN2cfn0LgdRWP2MRqzP6dejtlhFMa5QGnU7TJCGFRijZkVAL0JPvWHsf94M1sEbAFecfdQchCc+vYm4HBI+z/CgZfNbL6ZXRNShkJgK/C3yNeUE8wsJaQsR4wFJtX1Tt19A/AboATYBOxy95frOgdB52GImWWaWTJwEZAfQo66pDH7C2jcBjRmR9OYHVGfx2wtvosBZtYUeAr4L3f/JIwM7n7I3YuAPGCAmXWv6wxmdgmwxd3n1/W+T2Cwu/cBLgT+08yGhpAhgeCr4/9z997AHiCU+Z0AZpYEjAKeDGHf6QRdykKgNZBiZt+q6xzuvgL4FfAy8CKwCDhU1zkkfBq3NWafgMbsY/uut2N2GIXxBo6v1vMi205LZpZIMLj+092nhZ0n8rXPG8AFIex+EDDKzNYTfF073Mz+EUKOI592cfctwNMEXyfXtTKgLKoLNJVg0A3LhcACd98cwr7PB9a5+1Z3PwhMA84OIQfu/rC793X3ocAOgjmmDZnG7Eo0bh+lMft4GrOPqbdjdhiF8Tygo5kVRj7NjAWmh5AjdGZmBHORVrj7fSHmyDaztMj1JsAI4IO6zuHut7p7nrsXEPx/8bq71/knTDNLMbNmR64DIwm+jqlT7v4xUGpmnSObzgOW13WOKOMI4Su5iBLgTDNLjvy7OY9gbmedM7OcyM82BHPVJoaRow5pzI6icfsYjdnH05h9nHo7ZifURaho7l5hZtcBLxGslnzE3ZfVdQ4zmwScC2SZWRnwv+7+cB3HGARcCSyJzBMDuM3dX6jjHK2Ax8wsnuDD0hR3D+2wOzGgBfB08G+ZBGCiu78YUpYfAf+MFCRrge+GESLyx2YEcG0Y+3f3OWY2FVgAVAALCe80o0+ZWSZwEPjPGFhcU6s0Zn+Oxu3YozG7Eo3ZxzmpMVunhBYRERERQYvvREREREQAFcYiIiIiIoAKYxERERERQIWxiIiIiAigwlhEREREBFBhLCIiIiICqDAWEREREQHg/wNsRptJhXnr8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
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     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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     "data": {
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       "<br/>Waiting for W&B process to finish, PID 2612<br/>Program ended successfully."
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    {
     "data": {
      "text/html": [
       "Find user logs for this run at: <code>/content/wandb/run-20210922_062202-3fj5gqmc/logs/debug.log</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Find internal logs for this run at: <code>/content/wandb/run-20210922_062202-3fj5gqmc/logs/debug-internal.log</code>"
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       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>0.72425</td></tr><tr><td>epoch</td><td>10</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>eps_1</td><td>1e-05</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>lr_1</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.95</td></tr><tr><td>mom_1</td><td>0.95</td></tr><tr><td>raw_loss</td><td>0.36197</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>sqr_mom_1</td><td>0.99</td></tr><tr><td>train_loss</td><td>0.49017</td></tr><tr><td>valid_loss</td><td>0.88908</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>wd_1</td><td>0.01</td></tr></table>"
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       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>accuracy</td><td>▁▂▄▆▆▆▇▇██</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>lr_1</td><td>▁▁▂▃▄▅▆▇███████▇▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>mom_0</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>mom_1</td><td>██▇▆▅▄▃▂▁▁▁▁▁▁▁▂▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇▇█████</td></tr><tr><td>raw_loss</td><td>█▇▆▅▄▄▄▄▄▄▄▄▃▃▃▃▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▃▁▁▂▂▁▁</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>█▇▇▆▆▅▅▅▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁</td></tr><tr><td>valid_loss</td><td>██▅▂▂▂▁▁▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>wd_1</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
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    {
     "data": {
      "text/html": [
       "Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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     "data": {
      "text/html": [
       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">swift-wave-44</strong>: <a href=\"https://wandb.ai/timeseriesai/LSST_v01/runs/3fj5gqmc\" target=\"_blank\">https://wandb.ai/timeseriesai/LSST_v01/runs/3fj5gqmc</a><br/>\n",
       "                "
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   ],
   "source": [
    "for ks in [None, 1, 3, 5, 7, [1, 3, 5, 7]]:\n",
    "\n",
    "    config = AttrDict (\n",
    "        batch_tfms = TSStandardize(by_sample=True),\n",
    "        arch = TSiTPlus,\n",
    "        arch_config = dict(ks=ks),\n",
    "        lr = 1e-3,\n",
    "        n_epoch = 10,   \n",
    "    )\n",
    "\n",
    "    with wandb.init(project=\"LSST_v01\", config=config, group='kss'):\n",
    "        X, y, splits = get_UCR_data('LSST', split_data=False)\n",
    "        tfms = [None, TSClassification()]\n",
    "        cbs = [ShowGraph(), WandbCallback(log_preds=False, log_model=False, dataset_name='LSST')] \n",
    "        learn = TSClassifier(X, y, splits=splits, tfms=tfms, batch_tfms=config.batch_tfms, arch=config.arch, \n",
    "                            arch_config=config.arch_config, metrics=accuracy, cbs=cbs)\n",
    "        learn.fit_one_cycle(config.n_epoch, config.lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case we've learned that setting a ks parameter significantly improves performance. That's a great finding!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`wandb` is a great tool that allows you to easily track your experiments. \n",
    "\n",
    "It's super-flexible. It allows you to track your dataset, data preprocessing, data transforms, architecutres, architecture configurations, training loop, etc. \n",
    "\n",
    "And you can group runs in projects to easily compare them. \n",
    "\n",
    "Here's all the code you need to start running experiments with W&B and `tsai`: \n",
    "\n",
    "```\n",
    "from tsai.all import *\n",
    "from fastai.callback.wandb import *\n",
    "import wandb\n",
    "\n",
    "config = AttrDict (\n",
    "    batch_tfms = TSStandardize(by_sample=True),\n",
    "    arch = TSiTPlus,\n",
    "    arch_config = {},\n",
    "    lr = 1e-3,\n",
    "    n_epoch = 10,   \n",
    ")\n",
    "\n",
    "with wandb.init(project=\"LSST_v01\", config=config, name='baseline'):\n",
    "    X, y, splits = get_UCR_data('LSST', split_data=False)\n",
    "    tfms = [None, TSClassification()]\n",
    "    cbs = [ShowGraph(), WandbCallback(log_preds=False, log_model=False, dataset_name='LSST')] \n",
    "    learn = TSClassifier(X, y, splits=splits, tfms=tfms, batch_tfms=config.batch_tfms, arch=config.arch, \n",
    "                         arch_config=config.arch_config, metrics=accuracy, cbs=cbs)\n",
    "    learn.fit_one_cycle(config.n_epoch, config.lr)\n",
    "```\n",
    "\n",
    "We've seen here just a small amount of everything W&B has to offer. I hope you'll start benefiting from it!"
   ]
  }
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